Tissue molecular signatures of kidney transplant rejections

ABSTRACT

By a genome-wide gene analysis of expression profiles of known or putative gene sequences in kidney biopsy samples, the present inventors have identified a consensus set of gene expression-based molecular biomarkers for distinguishing kidney transplantation patients who have Acute Rejection (AR), Acute Dysfunction No Rejection (ADNR), Chronic Allograft Nephropathy (CAN), or Transplant Excellent/Normal (TX). These molecular biomarkers are useful for diagnosis, prognosis and monitoring of transplantation patients.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/313,215 (filed Nov. 22, 2016, now pending), which is a § 371 U.S.national phase filing of PCT International Patent Application No.PCT/US2015/032,195 (filed May 22, 2015, now expired), which claims thebenefit of priority to U.S. application Ser. No. 14/481,167 (filed Sep.9, 2014, now abandoned); to International Application No.PCT/US2014/054735 (filed Sep. 9, 2014, now expired); to U.S. ProvisionalApplication No. 62/029,038 (filed Jul. 25, 2014, now expired); to U.S.Provisional Application No. 62/001,889 (filed May 22, 2014, nowexpired); to U.S. Provisional Application No. 62/001,902 (filed May 22,2014, now expired); and to U.S. Provisional Application No. 62/001,909(filed May 22, 2014, now expired). Each of the aforementioned priorityapplications is incorporated by reference herein in its entirety.

STATEMENT CONCERNING GOVERNMENT SUPPORT

This invention was made in part with the U.S. government support by theNational Institutes of Health Grant No. AI063603. The U.S. Governmenttherefore may have certain rights in the invention.

COPYRIGHT NOTIFICATION

Pursuant to 37 C.F.R. § 1.71(e), Applicants note that a portion of thisdisclosure contains material which is subject to copyright protection.The copyright owner has no objection to the facsimile reproduction byanyone of the patent document or patent disclosure, as it appears in thePatent and Trademark Office patent file or records, but otherwisereserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

Kidney transplantation offers a significant improvement in lifeexpectancy and quality of life for patients with end stage renaldisease. Unfortunately, graft losses due to allograft dysfunction orother uncertain etiologies have greatly hampered the therapeuticpotential of kidney transplantation. Currently, immune reactivity ofkidney transplant recipients is estimated by monitoring the levels ofimmunosuppressive drugs, and by functional and/or histologicalevaluation of the allograft. Diagnosis of acute rejection relies onclinical data (e.g., patient signs and symptoms) and laboratory datasuch as tissue biopsy. The laboratory pathologist generally seeks threemain histological signs: (1) infiltrating T cells, perhaps accompaniedby infiltrating eosinophils, plasma cells, and neutrophils, particularlyin telltale ratios, (2) structural compromise of tissue anatomy, varyingby tissue type transplanted, and (3) injury to blood vessels.

There is a need in the art for alternative and more effective means thatcan diagnose and directly quantify the extent of the recipient's immuneresponse towards the allograft. Such means would help clinicians tocustomize the prescription of immunosuppressive drugs to individualpatients. The present invention addresses this and other unfulfilledneeds in the art.

SUMMARY OF THE INVENTION

In one aspect, the invention provides methods of prognosing, detecting,diagnosing or monitoring a kidney transplant rejection or injury, orlack thereof in a subject. These methods may involve obtaining nucleicacids of interest, and then (a) determining expression levels in asubject of at least 5 genes selected from the genes listed in Table 7,Table 8, Table 9, Table 10, or Table 11; and (b) detecting, prognosing,diagnosing or monitoring from the expression levels of the genes anongoing transplant rejection or injury, or lack thereof in the subject.In some embodiments, the nucleic acids of interest comprise mRNAextracted from a sample from a subject or nucleic acids derived from themRNA extracted from the sample from the subject. In some embodiments,the nucleic acids are contacted with probes, wherein the probes arespecific for the at least five genes. In some preferred embodiments,step (a) is performed on a biopsy sample of the subject, particularly akidney biopsy of the subject.

Some of the methods are directed to subjects who have acute rejection(AR), acute dysfunction no rejection (ADNR), chronic allograftnephropathy (CAN), or well-functioning normal transplant (TX). In someof the methods, for each of the at least five genes, step (b) entailscomparing the expression level of the gene in the subject to one or morereference expression levels of the gene associated with AR, ADNR, CAN,or TX. In some methods, step (b) further involves for each of the atleast five genes assigning the expression level of the gene in thesubject a value or other designation which can provide an indicationwhether the subject has AR, ADNR, CAN, or TX. In some of these methods,the expression level of each of the at least five genes is assigned avalue on a normalized scale of values associated with a range ofexpression levels in kidney transplant patients with AR, ADNR, CAN, orTX. In some methods, the expression level of each of the at least fivegenes is assigned a value or other designation in order to provide anindication that the subject has or is at risk of AR, ADNR, CAN, haswell-functioning normal transplant, or that the expression level isuninformative. In some methods, step (b) further entails combining thevalues or designations for each of the genes to provide a combined valueor designation which can provide an indication whether the subject hasor is at risk of AR, ADNR, CAN, or has well-functioning normaltransplant (TX).

In some methods of the invention, the steps are repeated at differenttimes on the subject. Some of these methods are directed to subjects whoare receiving a drug. In some of these methods, a change in the combinedvalue or designation over time provides an indication of theeffectiveness of the drug. In some methods of the invention, the subjecthas undergone a kidney transplant within 1 month, 3 months, 1 year, 2years, 3 years or 5 years of performing step (a). In some methods, step(a) is performed on at least 10, 20, 40, or 100 genes. Some of themethods further include changing the treatment regime of the subjectresponsive to the prognosing, detecting, diagnosing or monitoring step.In some of these methods, the subject has received a drug beforeperforming the methods, and the change involves administering anadditional drug or administering a higher dose of the same drug, oradministering a lower dose of the same drug, or stopping administeringthe same drug.

Some methods of the invention utilize the genes listed in Table 7 forprognosing or diagnosing subjects who have AR, ADNR, CAN, or TX. Someother methods of the invention utilize the genes listed in Table 8 forprognosing or diagnosing subjects who have AR, ADNR, or TX. Still someother methods of the invention utilize the genes listed in Table 9 forprognosing or diagnosing subjects who have CAN or TX. Still some othermethods of the invention utilize the genes listed in Table 10 forprognosing or diagnosing subjects who have AR or TX. Still some othermethods of the invention utilize the genes listed in Table 11 forprognosing or diagnosing subjects who have CAN/IFTA or TX. In somemethods, expression levels of the genes are determined at the mRNA levelor at the protein level. In some methods, step (b) is performed by acomputer.

In another aspect, the invention provides an array which contains asupport or supports bearing a plurality of nucleic acid probescomplementary to a plurality of mRNAs fewer than 5000 in number.Typically, the plurality of mRNAs includes mRNAs expressed by at leastfive genes selected from Table 7, Table 8, Table 9, Table 10, or Table11. Some of the arrays contain a plurality of mRNAs that are fewer than1000 or fewer than 100 in number. In some arrays, the plurality ofnucleic acid probes are attached to a planar support or to beads. In arelated aspect, the invention provides an array which contains supportor supports bearing a plurality of ligands that specifically bind to aplurality of proteins fewer than 5000 in number. Typically, theplurality of proteins include at least five proteins encoded by genesselected from Table 7, Table 8, Table 9, Table 10, or Table 11. Some ofthese arrays contain ligands that specifically bind to a plurality ofproteins that are fewer than 1000 or fewer than 100 in number. In somearrays, the plurality of ligands are attached to a planar support or tobeads. In some of the arrays, the ligands are different antibodies thatbind to different proteins of the plurality of proteins.

In another aspect, the invention provides methods of expressionanalysis. The methods involve determining expression levels of up to5000 genes in a sample from a subject having a kidney transplant.Typically, the genes include at least 5 genes selected from Table 7,Table 8, Table 9, Table 10, or Table 11. In some methods, the expressionlevels of up to 100 or 1000 genes are determined. In some methods, theexpression levels are determined at the mRNA level or at the proteinlevel. In some methods, the expression levels are determined byquantitative PCR or hybridization to an array or RNA sequencing.

In still another aspect, the invention provides methods of screening acompound for activity in inhibiting or treating a kidney transplantrejection or injury. The methods involve (a) administering the compoundto a subject having or at risk of developing a kidney transplantrejection; (b) determining expression levels of at least five genes inthe subject selected from Tables 1-11 and species variants thereofbefore and after administering the compound to the subject, (c)determining whether the compound has activity in inhibiting or treatingthe kidney transplant rejection from a change in expression levels ofthe genes after administering the compound. In some of these methods,the kidney transplant rejection or injury to be treated or inhibited isAR, ADNR, or CAN. In some methods, step (c) involves for each of the atleast five changes assigning a value or designation depending on whetherthe change in the expression level of the gene relative to one or morereference levels indicating presence or absence of the kidney transplantrejection. Some of these methods further entail determining a combinedvalue or designation for the at least five genes from the values ordesignations determined for each gene. Some of the methods employsubjects who are human or nonhuman animal models of the kidneytransplant rejection.

In another aspect, the methods disclosed herein have an error rate ofless than about 40%. In some embodiments, the method has an error rateof less than about 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 2%, or 1%.For example, the method has an error rate of less than about 10%. Insome embodiments, the methods disclosed herein have an accuracy of atleast about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example,the method has an accuracy of at least about 70%. In some embodiments,the methods disclosed herein have a sensitivity of at least about 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has asensitivity of at least about 80%. In some embodiments, the methodsdisclosed herein have a positive predictive value of at least about 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. In some embodiments, themethods disclosed herein have a negative predictive value of at leastabout 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.

In some embodiments, the gene expression products described herein areRNA (e.g., mRNA). In some embodiments, the gene expression products arepolypeptides. In some embodiments, the gene expression products are DNAcomplements of RNA expression products from the transplant recipient.

In an embodiment, the algorithm described herein is a trained algorithm.In another embodiment, the trained algorithm is trained with geneexpression data from biological samples from at least three differentcohorts. In another embodiment, the trained algorithm comprises a linearclassifier. In another embodiment, the linear classifier comprises oneor more linear discriminant analysis, Fisher's linear discriminant,Naïve Bayes classifier, Logistic regression, Perceptron, Support vectormachine (SVM) or a combination thereof. In another embodiment, thealgorithm comprises a Diagonal Linear Discriminant Analysis (DLDA)algorithm. In another embodiment, the algorithm comprises a NearestCentroid algorithm. In another embodiment, the algorithm comprises aRandom Forest algorithm or statistical bootstrapping. In anotherembodiment, the algorithm comprises a Prediction Analysis of Microarrays(PAM) algorithm. In another embodiment, the algorithm is not validatedby a cohort-based analysis of an entire cohort. In another embodiment,the algorithm is validated by a combined analysis with an unknownphenotype and a subset of a cohort with known phenotypes.

In another aspect, the assay is a microarray, SAGE, blotting, RT-PCR,sequencing and/or quantitative PCR assay. In another embodiment, theassay is a microarray assay. In another embodiment, the microarray assaycomprises the use of an Affymetrix Human Genome U133 Plus 2.0 GeneChip.In another embodiment, the mircroarray uses the Hu133 Plus 2.0 cartridgearrays plates. In another embodiment, the microarray uses the HTHG-U133+PM array plates. In another embodiment, the assay is asequencing assay. In another embodiment, the assay is a RNA sequencingassay.

In some embodiments, the transplant recipient has a normal serumcreatinine level. In some cases, the transplant recipient has anelevated serum creatinine level. In some cases, the transplant recipienthas a serum creatinine level of at least 0.4 mg/dL, 0.6 mg/dL, 0.8mg/dL, 1.0 mg/dL, 1.2 mg/dL, 1.4 mg/dL, 1.6 mg/dL, 1.8 mg/dL, 2.0 mg/dL,2.2 mg/dL, 2.4 mg/dL, 2.6 mg/dL, 2.8 mg/dL, 3.0 mg/dL, 3.2 mg/dL, 3.4mg/dL, 3.6 mg/dL, 3.8 mg/dL, or 4.0 mg/dL. For example, the transplantrecipient has a serum creatinine level of at least 1.5 mg/dL. In anotherexample, the transplant recipient has a serum creatinine level of atleast 3 mg/dL. In another example, the transplant recipient has a serumcreatinine level less than 3 mg/dL, less than 2 mg/dL, less than 1.5mg/dL, or less than 1.0 mg/dL.

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic overview of certain methods in the disclosure.

FIG. 2 shows a schematic overview of certain methods of acquiringsamples, analyzing results, and transmitting reports over a computernetwork.

FIG. 3 shows a computer system for implementing the methods of thedisclosure.

DETAILED DESCRIPTION

The present invention is predicated in part on the development ofmolecular classifiers that can diagnose kidney transplantation patientswho are Acute Rejection (AR), Acute Dysfunction No Rejection (ADNR),Chronic Allograft Nephropathy/Chronic Rejection (CAN/IFTA; CR), orTransplant Excellent/Normal (TX). The molecular classifiers wereidentified using RNA from kidney biopsies of the patients. Theseclassifiers were successfully validated in an independent cohort,underscoring their applicability in significantly differentracial/ethnic backgrounds as well as significantly different drugregimens. These signatures also correlated as well or better with assaysbased on creatinine levels and histology-based predictions. As detailedherein, they provide molecular insights into disease pathogenesis andfunctional molecular pathways including possible new drug targets.

The invention accordingly provides molecular diagnostic assays based onthe identified molecular classifiers and additional studies of theinventors. The assays are cost effective, labor-saving and completelyobjective as compared to conventional light histology, the current “goldstandard”. It also provides a molecular score for phenotypes that arevery difficult to accomplish with light histology. Thus, methods of theinvention can be used as an alternative or complement to light histologyin order to make more informed therapy decisions. They provide practicaladvantages of an automated, rapid molecular-based diagnostic over thecurrent workflow for light histology involving pathologists making theinterpretations. In addition to the diagnostic utilities, such assayscould also provide a more in-depth understanding of the complexmechanisms of acute rejection, chronic injury, and tolerance in organtransplantation. This would allow the design of new and potentially moreeffective strategies for the minimization of immunosuppression, or evenfor the induction of immunological tolerance.

An overview of certain methods in the disclosure is provided in FIG. 1.In some instances, a method comprises obtaining a sample from atransplant recipient in an invasive manner (110), such as via a biopsy,etc. The sample may comprise gene expression products (e.g.,polypeptides, RNA, mRNA isolated from within cells or a cell-freesource) associated with the status of the transplant (e.g., AR, ADNR,normal transplant function, etc.). In some instances, the method mayinvolve reverse-transcribing RNA within the sample to obtain cDNA thatcan be analyzed using the methods described herein. The method may alsocomprise assaying the level of the gene expression products (or thecorresponding DNA) using methods such as microarray or sequencingtechnology (120). The method may also comprise applying an algorithm tothe assayed gene expression levels (130).

The following sections provide guidance for carrying out the methods ofthe invention.

I. Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by those of ordinary skillin the art to which this invention pertains. The following referencesprovide one of skill with a general definition of many of the terms usedin this invention: Academic Press Dictionary of Science and Technology,Morris (Ed.), Academic Press (1^(st) ed., 1992); Illustrated Dictionaryof Immunology, Cruse (Ed.), CRC Pr I LIc (2^(nd) ed., 2002); OxfordDictionary of Biochemistry and Molecular Biology, Smith et al. (Eds.),Oxford University Press (revised ed., 2000); Encyclopaedic Dictionary ofChemistry, Kumar (Ed.), Anmol Publications Pvt. Ltd. (2002); Dictionaryof Microbiology and Molecular Biology, Singleton et al. (Eds.), JohnWiley & Sons (3^(rd) ed., 2002); Dictionary of Chemistry, Hunt (Ed.),Routledge (1^(st) ed., 1999); Dictionary of Pharmaceutical Medicine,Nahler (Ed.), Springer-Verlag Telos (1994); Dictionary of OrganicChemistry, Kumar and Anandand (Eds.), Anmol Publications Pvt. Ltd.(2002); and A Dictionary of Biology (Oxford Paperback Reference), Martinand Hine (Eds.), Oxford University Press (4^(th) ed., 2000). Inaddition, the following definitions are provided to assist the reader inthe practice of the invention.

Transplantation is the transfer of tissues, cells or an organ from adonor into a recipient. If the donor and recipient as the same person,the graft is referred to as an autograft and as is usually the casebetween different individuals of the same species an allograft. Transferof tissue between species is referred to as a xenograft. Unlessotherwise noted, all graft samples described herein were allografts.

A biopsy is a specimen obtained from a living patient for diagnostic orprognostic evaluation. Kidney biopsies can be obtained with a needle.

An average value can refer to any of a mean, median or mode.

Chronic allograft nephropathy (CAN), also known as sclerosing/chronicallograft nephropathy, is the leading cause of kidney transplant failureand happens month to years after the transplant. It is characterized bya gradual decline in kidney function and, typically, accompanied by highblood pressure and hematuria. The histopathology is characterized byinterstitial fibrosis, tubular atrophy, fibrotic intimal thickening ofarteries and glomerulosclerosis.

A gene expression level is associated with a particular phenotype e.g.,presence of acute graft rejection if the gene is differentiallyexpressed in a patient having the phenotype relative to a patientlacking the phenotype to a statistically significant extent. Unlessotherwise apparent from the context a gene expression level can bemeasured at the mRNA and/or protein level.

A target nucleic acids is a nucleic acid (often derived from abiological sample), to which a polynucleotide probe is designed tospecifically hybridize. The probe can detect presence, absence and/oramount of the target. The term can refer to the specific subsequence ofa larger nucleic acid to which the probe is directed or to the overallsequence (e.g., cDNA or mRNA) whose expression level is to be detected.The term can also refer to a nucleic acid that is analyzed by a method,including sequencing, PCR, or other method known in the art.

The term subject or patient can include human or non-human animals.Thus, the methods and described herein are applicable to both human andveterinary disease and animal models. Preferred subjects are “patients,”i.e., living humans that are receiving medical care for a disease orcondition. This includes persons with no defined illness who are beinginvestigated for signs of pathology. The term subject or patient caninclude transplant recipients or donors or healthy subjects. The methodscan be particularly useful for human subjects who have undergone akidney transplant although they can also be used for subjects who havegone other types of transplant (e.g., heart, liver, lung, stem cell,etc.). The subjects may be mammals or non-mammals. Preferably, thesubject is a human but in some cases, the subject is a non-human mammal,such as a non-human primate (e.g., ape, monkey, chimpanzee), cat, dog,rabbit, goat, horse, cow, pig, rodent, mouse, SCID mouse, rat, guineapig, or sheep. The subject may be male or female; the subject may beand, in some cases, the subject may be an infant, child, adolescent,teenager or adult. In some cases, the methods provided herein are usedon a subject who has not yet received a transplant, such as a subjectwho is awaiting a tissue or organ transplant. In other cases, thesubject is a transplant donor. In some cases, the subject has notreceived a transplant and is not expected to receive such transplant. Insome cases, the subject may be a subject who is suffering from diseasesrequiring monitoring of certain organs for potential failure ordysfunction. In some cases, the subject may be a healthy subject.

Often, the subject is a patient or other individual undergoing atreatment regimen, or being evaluated for a treatment regimen (e.g.,immunosuppressive therapy). However, in some instances, the subject isnot undergoing a treatment regimen. A feature of the graft tolerantphenotype detected or identified by the subject methods is that it is aphenotype which occurs without immunosuppressive therapy, e.g., it ispresent in a subject that is not receiving immunosuppressive therapy.

A transplant recipient may be a recipient of a solid organ or a fragmentof a solid organ such as a kidney. Preferably, the transplant recipientis a kidney transplant or allograft recipient. In some instances, thetransplant recipient may be a recipient of a tissue or cell. In someparticular examples, the transplanted kidney may be a kidneydifferentiated in vitro from pluripotent stem cell(s) (e.g., inducedpluripotent stem cells or embryonic stem cells).

The donor organ, tissue, or cells may be derived from a subject who hascertain similarities or compatibilities with the recipient subject. Forexample, the donor organ, tissue, or cells may be derived from a donorsubject who is age-matched, ethnicity-matched, gender-matched,blood-type compatible, or HLA-type compatible with the recipientsubject.

In various embodiments, the subjects suitable for methods of theinvention are patients who have undergone an organ transplant within 6hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 15days, 20 days, 25 days, 1 month, 2 months, 3 months, 4 months, 5 months,7 months, 9 months, 11 months, 1 year, 2 years, 4 years, 5 years, 10years, 15 years, 20 years or longer of prior to receiving aclassification obtained by the methods disclosed herein, such asdetection of subAR.

Diagnosis refers to methods of estimating or determining whether or nota patient is suffering from a given disease or condition or severity ofthe condition. Diagnosis does not require ability to determine thepresence or absence of a particular disease with 100% accuracy, or eventhat a given course or outcome is more likely to occur than not.Instead, the “diagnosis” refers to an increased probability that acertain disease or condition is present in the subject compared to theprobability before the diagnostic test was performed. Similarly, aprognosis signals an increased probability that a given course oroutcome will occur in a patient relative to the probability before theprognostic test.

A probe or polynucleotide probe is a nucleic acid capable of binding toa target nucleic acid of complementary sequence through one or moretypes of chemical bonds, usually through complementary base pairing,usually through hydrogen bond formation, thus forming a duplexstructure. The probe binds or hybridizes to a “probe binding site.” Aprobe can include natural (e.g., A, G, C, U, or T) or modified bases(e.g., 7-deazaguanosine, inosine.). A probe can be an oligonucleotidewhich is a single-stranded DNA. Polynucleotide probes can be synthesizedor produced from naturally occurring polynucleotides. In addition, thebases in a probe can be joined by a linkage other than a phosphodiesterbond, so long as it does not interfere with hybridization. Thus, probescan include, for example, peptide nucleic acids in which the constituentbases are joined by peptide bonds rather than phosphodiester linkages(see, e.g., Nielsen et al., Science 254, 1497-1500 (1991)). Some probescan have leading and/or trailing sequences of noncomplementarityflanking a region of complementarity.

A perfectly matched probe has a sequence perfectly complementary to aparticular target sequence. The probe is typically perfectlycomplementary to a portion (subsequence) of a target sequence. The term“mismatch probe” refers to probes whose sequence is deliberatelyselected not to be perfectly complementary to a particular targetsequence.

The term “isolated,” “purified” or “substantially pure” means an objectspecies (e.g., a nucleic acid sequence described herein or a polypeptideencoded thereby) has been at least partially separated from thecomponents with which it is naturally associated.

Differential expression refers to a statistically significant differencein expression levels of a gene between two populations of samples (e.g.,kidney tissue samples with and without a specific type of kidney graftrejection such as AR). The expression levels can differ for example byat least a factor of 1.5 or 2 between such populations of samples.Differential expression includes genes that are expressed in onepopulation and are not expressed (at least at detectable levels) in theother populations. Unique expression refers to detectable expression inone population and undetectable expression (i.e., insignificantlydifferent from background) in the other population using the sametechnique (e.g., as in the present example for detection).

Control populations for comparison with populations undergoing a graftrejection or injury (e.g., AR) are usually referred to as being withoutthe rejection or injury. Unless otherwise indicated, such a controlpopulation also means subjects without acute kidney rejection and/orchronic rejection.

Hybridization reactions are preferably performed under stringentconditions in which probes or primers hybridize to their intended targetwith which they have perfect complementarity and not to or at least to areduced extent to other targets. An example of stringent hybridizationconditions are hybridization in 6×sodium chloride/sodium citrate (SSC)at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at50° C., 55° C., 60° C., and even more or 65° C.

Statistical significance means p<0.05, <0.01, or <0.005, or even <0.001level.

The term “about,” as used herein and throughout the disclosure,generally refers to a range that may be 15% greater than or 15% lessthan the stated numerical value within the context of the particularusage. For example, “about 10” would include a range from 8.5 to 11.5.

The term “or” as used herein and throughout the disclosure, generallymeans “and/or”.

II. Genes in Profiles

Table 7 lists 199 genes whose expression changes significantly in kidneybiopsies between transplant patients undergoing acute rejection (AR),acute dysfunction no rejection (ADNR), chronic allograft nephropathy(CAN), or are transplant excellent (i.e., with normal functionaltransplant) (TX). The columns in the table have the following meanings:column 1 is a number assigned to a gene, column 2 is an Affymetrixnumber indicating a set of probes suitable for measuring expression ofthe gene, column 3 is a gene designation number, column 4 is a gene name(recognized names of HUGO or similar bodies are used when available),column 5 is a further description of the gene, column 6 is a measure ofthe statistical significance of change in gene expression between theabove patient populations, and columns 7-10 respectively show meanexpression levels of ADNR, AR, CAN, and TX patients. Expression profilesof genes selected from this list can be used to distinguish kidneytransplant patients with one of the above-noted four graft conditions orphenotypes (4-way prediction).

Table 8 provides similar information on 197 genes that show differentialexpression between kidney transplant patients undergoing acute rejection(AR), acute dysfunction no rejection (ADNR), or are transplant excellent(TX). These are the most common phenotypes of kidney transplant duringthe early post-transplant period, while CAN is usually a latemanifestation of graft injury which is progressive. Expression profilesof genes selected from this Table are thus useful for distinguishingkidney transplant patients who are undergoing acute rejection (AR),acute dysfunction no rejection (ADNR), or are transplant excellent (TX)(3-way prediction). Table 9 similarly lists information on 200 geneswhich show differential expression between transplant patients who havechronic allograft nephropathy (CAN) and patients who are transplantexcellent (TX). Table 10 lists information on genes which showdifferential expression between transplant patients who have acuterejection (AR) and patients who are transplant excellent (TX). Table 11lists information on genes which show differential expression betweentransplant patients who have chronic allograft nephropathy(CAN)/interstitial fibrosis and tubular atrophy (IF/TA) and patients whoare transplant excellent (TX). Expression profiles of genes selectedfrom this list are typically suitable for making 2-way diagnosis betweenpatients with these two phenotypes (4-way prediction).

The genes referred to in the above tables are human genes. In somemethods, species variants or homologs of these genes are used in anon-human animal model. Species variants are the genes in differentspecies having greatest sequence identity and similarity in functionalproperties to one another. Many species variants of the above humangenes are listed in the Swiss-Prot database.

Raw gene expression levels are comparable between different genes in thesame sample but not necessarily between different samples. As notedabove, values given for gene expression levels can be normalized so thatvalues for particular genes are comparable within and between thepopulations being analyzed. The normalization eliminates or at leastreduces to acceptable levels any sample to sample differences arisingfrom factors other than graft rejection (e.g., differences in overalltranscription levels of patients due to general state of health anddifferences in sample preparation or nucleic acid amplification betweensamples) and also technical variation among the samples being analyzed.The normalization effectively applies a correction factor to themeasured expression levels from a given array such that a profile ofmany expression levels in the array are the same between differentpatient samples. Software for normalizing overall expression patternsbetween different samples is both commercially (e.g., Partek GenomicsSuite from Partek Systems) and publically available (e.g., XRAY fromBiotique Systems or BRB ArrayTools from the National Cancer Institute).After applying appropriate normalizing factors to the measuredexpression value of a particular gene in different samples, an averageor mean value of the expression level is determined for the samples in apopulation. The average or mean values between different populations arethen compared to determine whether expression level has changedsignificantly between the populations. The changes in expression levelindicated for a given gene represent the relative expression level ofthat gene in samples from a population of individuals with a definedcondition (e.g., transplant patients with AR of specified Banff stage)relative to samples from a control population (kidney transplantpatients not undergoing rejection). Similar principles apply innormalizing gene expression levels at the mRNA and protein levels.Comparisons between populations are made at the same level (e.g., mRNAlevels in one population are compared with mRNA levels in anotherpopulation or protein levels in one population with protein levels inanother population).

III. Subject Populations

The methods of the invention are suitable for detecting anddistinguishing different types of graft rejections in kidney transplantpatients. The methods are particularly useful on human subjects who haveundergone a kidney transplant although can also be used on subjects whohave gone other types of transplant (e.g., heart, liver, lungs, stemcell) or on non-humans who have undergone kidney or other transplant. Asdetailed herein, the methods can be employed to distinguish transplantpatients who (1) have or are at risk of having acute rejection (AR), (2)have or are at risk of having acute dysfunction no rejection (ADNR), (3)have or are at risk of having chronic allograft nephropathy (CAN), or(4) have normal functioning transplant (TX). Other than patients forsuch a four way diagnosis, the subject population can also comprise onlypatients at early post-transplant period who are therefore likely tohave AR, have acute dysfunction no rejection (ADNR), or are transplantexcellent (TX). The patients are examined via a three-way analysis toidentify one of these three graft phenotypes. Further, the subjectpopulation can also merely contain late post-transplant patients wholikely either have chronic allograft nephropathy (CAN) or are transplantexcellent (TX). Such a subject population can be examined with methodsof the invention to diagnose or confirm that the patients havelate-manifestation of graft injury.

Regardless of the specific subject population, gene expression levels inthe patients can be measured, for example, within, one month, threemonths, six months, one year, two years, five years or ten years after akidney transplant. In some methods, gene expression levels aredetermined at regular intervals, e.g., every 3 months, 6 months or everyyear post-transplant, either indefinitely, or until evidence of one ofthe noted phenotype is observed, in which case the frequency ofmonitoring is sometimes increased. In some methods, baseline values ofexpression levels are determined in a subject before a kidney transplantin combination with determining expression levels at one or more timepoints thereafter. Similar methods can be practiced in non-humanspecies, in which cases, the expression levels measured are the speciesequivalent of the human genes referenced above.

Often the methods are used on a subject, preferably human, that is atransplant recipient. The methods may be used for detecting orpredicting a condition of the transplant recipient such as acuterejection (AR), acute dysfunction with no rejection (ADNR), chronicallograft nephropathy (CAN), interstitial fibrosis and tubular atrophy(IF/TA), subclinical rejection acute rejection (SubAR), etc. In somecases, the condition may be AR. In some cases, the condition may beADNR. In some cases, the condition may be SubAR. In some cases, thecondition may be transplant dysfunction. In some cases, the conditionmay be transplant dysfunction with no rejection. In some cases, thecondition may be acute transplant dysfunction.

Typically, when the patient does not exhibit symptoms or test results oforgan dysfunction or rejection, the transplant is considered a normalfunctioning transplant (TX: Transplant eXcellent). An unhealthytransplant recipient may exhibit signs of organ dysfunction and/orrejection (e.g., an increasing serum creatinine). However, a subject(e.g., kidney transplant recipient) with subclinical rejection may havenormal and stable organ function (e.g. normal creatinine level andnormal eGFR). In these subjects, at the present time, rejection may bediagnosed histologically through a biopsy. A failure to recognize,diagnose and treat subclinical AR before significant tissue injury hasoccurred and the transplant shows clinical signs of dysfunction could bea major cause of irreversible organ damage. Moreover, a failure torecognize a chronic, subclinical immune-mediated organ damage and afailure to make appropriate changes in immunosuppressive therapy torestore a state of effective immunosuppression in that patient couldcontribute to late organ transplant failure. The methods disclosedherein can reduce or eliminate these and other problems associated withtransplant rejection or failure.

Acute rejection (AR) occurs when transplanted tissue is rejected by therecipient's immune system, which damages or destroys the transplantedtissue unless immunosuppression is achieved. T-cells, B-cells and otherimmune cells as well as possibly antibodies of the recipient may causethe graft cells to lyse or produce cytokines that recruit otherinflammatory cells, eventually causing necrosis of allograft tissue. Insome instances, AR may be diagnosed by a biopsy of the transplantedorgan. In the case of kidney transplant recipients, AR may be associatedwith an increase in serum creatinine levels. The treatment of AR mayinclude using immunosuppressive agents, corticosteroids, polyclonal andmonoclonal antibodies, engineered and naturally occurring biologicalmolecules, and antiproliferatives. AR more frequently occurs in thefirst three to 12 months after transplantation but there is a continuedrisk and incidence of AR for the first five years post transplant andwhenever a patient's immunosuppression becomes inadequate for any reasonfor the life of the transplant.

Acute dysfunction with no rejection (ADNR) is an abrupt decrease or lossof organ function without histological evidence of rejection from atransplant biopsy. Kidney transplant recipients with ADNR will oftenexhibit elevated creatinine levels. Unfortunately, the levels of kidneydysfunction based on serum creatinines are usually not significantlydifferent between AR and ADNR subjects.

Another condition that can be associated with a kidney transplant ischronic allograft nephropathy (CAN), which is characterized by a gradualdecline in kidney function and, typically, accompanied by high bloodpressure and hematuria. Histopathology of patients with CAN ischaracterized by interstitial fibrosis, tubular atrophy, fibroticintimal thickening of arteries and glomerulosclerosis typicallydescribed as IFTA. CAN/IFTA usually happens months to years after thetransplant though increased amounts of IFTA can be present early in thefirst year post transplant in patients that have received kidneys fromolder or diseased donors or when early severe ischemia perfusion injuryor other transplant injury occurs. CAN is a clinical phenotypecharacterized by a progressive decrease in organ transplant function. Incontrast, IFTA is a histological phenotype currently diagnosed by anorgan biopsy. In kidney transplants, interstitial fibrosis (IF) isusually considered to be present when the supporting connective tissuein the renal parenchyma exceeds 5% of the cortical area. Tubular atrophy(TA) refers to the presence of tubules with thick redundant basementmembranes, or a reduction of greater than 50% in tubular diametercompared to surrounding non-atrophic tubules. In certain instances,finding interstitial fibrosis and tubular atrophy (IFTA) on the biopsymay be early indicators that predict the later organ dysfunctionassociated with the clinical phenotype of CAN. Immunologically, CAN/IFTAusually represents a failure of effective longterm immunosuppression andmechanistically it is immune-mediated chronic rejection (CR) and caninvolve both cell and antibody-mediated mechanisms of tissue injury aswell as activation of complement and other blood coagulation mechanismsand can also involve inflammatory cytokine-mediated tissue activationand injury.

Subclinical rejection (SubAR) (also known as SCAR) is generally acondition that is histologically identified as acute rejection butwithout concurrent functional deterioration. For kidney transplantrecipients, subclinical rejection (SubAR) is histologically definedacute rejection that is characterized by tubulointerstitial mononuclearinfiltration identified from a biopsy specimen, but without concurrentfunctional deterioration (variably defined as a serum creatinine notexceeding about 10%, 20% or 25% of baseline values). A SubAR subjecttypically shows normal and/or stable serum creatinine levels. SubAR isusually diagnosed through biopsies that are taken at a fixed time aftertransplantation (e.g. protocol biopsies or serial monitoring biopsies)which are not driven by clinical indications but rather by standards ofcare. SubAR may be subclassified by some into acute SubAR or a milderform called borderline SubAR (suspicious for acute rejection) based onthe biopsy histology.

In some instances, a normal serum creatinine level and/or a normalestimated glomerular filtration rate (eGFR) may indicate healthytransplant (TX) or subclinical rejection (SubAR). For example, typicalreference ranges for serum creatinine are 0.5 to 1.0 mg/dL for women and0.7 to 1.2 mg/dL for men, though typical kidney transplant patients havecreatinines in the 0.8 to 1.5 mg/dL range for women and 1.0 to 1.9 mg/dLrange for men. This may be due to the fact that most kidney transplantpatients have a single kidney. In some instances, the trend of serumcreatinine levels over time can be used to evaluate the recipient'sorgan function. This is why it may be important to consider both“normal” serum creatinine levels and “stable” serum creatinine levels inmaking clinical judgments, interpreting testing results, deciding to doa biopsy or making therapy change decisions including changingimmunosuppressive drugs. For example, the transplant recipient may showsigns of a transplant dysfunction or rejection as indicated by anelevated serum creatinine level and/or a decreased eGFR. In someinstances, a transplant subject with a particular transplant condition(e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatininelevel of at least 0.1 mg/dL, 0.2 mg/dL, 0.3 mg/dL, 0.4 mg/dL, 0.5 mg/dL,0.6 mg/dL, 0.7 mg/dL 0.8 mg/dL, 0.9 mg/dL, 1.0 mg/dL, 1.1 mg/dL, 1.2mg/dL, 1.3 mg/dL, 1.4 mg/dL, 1.5 mg/dL, 1.6 mg/dL, 1.7 mg/dL, 1.8 mg/dL,1.9 mg/dL, 2.0 mg/dL, 2.1 mg/dL, 2.2 mg/dL, 2.3 mg/dL, 2.4 mg/dL, 2.5mg/dL, 2.6 mg/dL, 2.7 mg/dL, 2.8 mg/dL, 2.9 mg/dL, 3.0 mg/dL, 3.1 mg/dL,3.2 mg/dL, 3.3 mg/dL, 3.4 mg/dL, 3.5 mg/dL, 3.6 mg/dL, 3.7 mg/dL, 3.8mg/dL, 3.9 mg/dL, or 4.0 mg/dL. In some instances, a transplant subjectwith a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may havean increase of a serum creatinine level of at least 10%, 20%, 30%, 40%,50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some instances, atransplant subject with a certain transplant condition (e.g., AR, ADNR,CAN, etc.) may have an increase of a serum creatinine level of at least1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold,or 10-fold from baseline. In some cases, the increase in serumcreatinine (e.g., any increase in the concentration of serum creatininedescribed herein) may occur over about 0.25 days, 0.5 days, 0.75 days, 1day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, ormore. In some instances, a transplant subject with a particulartransplant condition (e.g., AR, ADNR, CAN, etc.) may have a decrease ofa eGFR of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%from baseline. In some cases, the decrease in eGFR may occur over 0.25days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4months, 5 months, or 6 months, or more. In some instances, diagnosing,predicting, or monitoring the status or outcome of a transplant orcondition comprises determining transplant recipient-specific baselinesand/or thresholds.

In some cases, the methods provided herein are used on a subject who hasnot yet received a transplant, such as a subject who is awaiting atissue or organ transplant. In other cases, the subject is a transplantdonor. In some cases, the subject has not received a transplant and isnot expected to receive such transplant. In some cases, the subject maybe a subject who is suffering from diseases requiring monitoring ofcertain organs for potential failure or dysfunction. In some cases, thesubject may be a healthy subject.

A transplant recipient may be a recipient of a solid organ or a fragmentof a solid organ. The solid organ may be a lung, kidney, heart, liver,pancreas, large intestine, small intestine, gall bladder, reproductiveorgan or a combination thereof. Preferably, the transplant recipient isa kidney transplant or allograft recipient. In some instances, thetransplant recipient may be a recipient of a tissue or cell. The tissueor cell may be amnion, skin, bone, blood, marrow, blood stem cells,platelets, umbilical cord blood, cornea, middle ear, heart valve, vein,cartilage, tendon, ligament, nerve tissue, embryonic stem (ES) cells,induced pluripotent stem cells (IPSCs), stem cells, adult stem cells,hematopoietic stem cells, or a combination thereof.

The donor organ, tissue, or cells may be derived from a subject who hascertain similarities or compatibilities with the recipient subject. Forexample, the donor organ, tissue, or cells may be derived from a donorsubject who is age-matched, ethnicity-matched, gender-matched,blood-type compatible, or HLA-type compatible with the recipientsubject.

The transplant recipient may be a male or a female. The transplantrecipient may be patients of any age. For example, the transplantrecipient may be a patient of less than about 10 years old. For example,the transplant recipient may be a patient of at least about 0, 5, 10,20, 30, 40, 50, 60, 70, 80, 90, or 100 years old. The transplantrecipient may be in utero. Often, the subject is a patient or otherindividual undergoing a treatment regimen, or being evaluated for atreatment regimen (e.g., immunosuppressive therapy). However, in someinstances, the subject is not undergoing a treatment regimen. A featureof the graft tolerant phenotype detected or identified by the subjectmethods is that it is a phenotype which occurs without immunosuppressivetherapy, e.g., it is present in a host that is not undergoingimmunosuppressive therapy such that immunosuppressive agents are notbeing administered to the host.

In various embodiments, the subjects suitable for methods of theinvention are patients who have undergone an organ transplant within 6hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 15days, 20 days, 25 days, 1 month, 2 months, 3 months, 4 months, 5 months,7 months, 9 months, 11 months, 1 year, 2 years, 4 years, 5 years, 10years, 15 years, 20 years or longer of prior to receiving aclassification disclosed herein (e.g., a classification obtained by themethods disclosed herein). Some of the methods further comprise changingthe treatment regime of the patient responsive to the detecting,prognosing, diagnosing or monitoring step. In some of these methods, thesubject can be one who has received a drug before performing themethods, and the change in treatment comprises administering anadditional drug, administering a higher or lower dose of the same drug,stopping administration of the drug, or replacing the drug with adifferent drug or therapeutic intervention.

The subjects can include transplant recipients or donors or healthysubjects. The methods can be useful on human subjects who have undergonea kidney transplant although can also be used on subjects who have goneother types of transplant (e.g., heart, liver, lung, stem cell, etc.).The subjects may be mammals or non-mammals. The methods can be useful onnon-humans who have undergone kidney or other transplant. Preferably,the subjects are a mammal, such as, a human, non-human primate (e.g.,apes, monkeys, chimpanzees), cat, dog, rabbit, goat, horse, cow, pig,rodent, mouse, SCID mouse, rat, guinea pig, or sheep. Even morepreferably, the subject is a human. The subject may be male or female;the subject may be a fetus, infant, child, adolescent, teenager oradult.

IV. Methods of Measuring Profiles

The preferred sample type for analysis with methods of the invention isa tissue biopsy, e.g., kidney biopsy for kidney transplant patients. Inaddition to biopsy samples, some other types of samples may also be usedin the practice of the invention. These include, e.g., blood sampleswhich can be whole blood or fractions thereof such as plasma orlymphocytes. Other samples that can be analyzed include urine, feces,and saliva. The samples are typically isolated from a subject and notreturned to the subject. The analytes of interests in the samples can beanalyzed with or without further processing of the sample, such aspurification and amplification.

Expression profiles are preferably measured at the nucleic acid level,meaning that levels of mRNA or nucleic acid derived therefrom (e.g.,cDNA or cRNA). An expression profile refers to the expression levels ofa plurality of genes in a sample. A nucleic derived from mRNA means anucleic acid synthesized using mRNA as a template. Methods of isolationand amplification of mRNA are well known in the art, e.g., as describedin WO 97/10365, WO 97/27317, Chapter 3 of Laboratory Techniques inBiochemistry and Molecular Biology: Hybridization With Nucleic AcidProbes, Part I. Theory and Nucleic Acid Preparation, (P. Tijssen, ed.)Elsevier, N.Y. (1993). If mRNA or a nucleic acid therefrom is amplified,the amplification is performed under conditions that approximatelypreserve the relative proportions of mRNA in the original samples, suchthat the levels of the amplified nucleic acids can be used to establishphenotypic associations representative of the mRNAs.

A variety of approaches are available for determining mRNA levelsincluding probe arrays and quantitative PCR. A number of distinct arrayformats are available. Some arrays, such as an Affymetrix GeneChip®array, have different probes occupying discrete known areas of acontiguous support. Other arrays, such as arrays from Illumina, havedifferent probes attached to different particles or beads. In sucharrays, the identity of which probe is attached to which particle orbeads is usually determinable from an encoding system. The probes can beoligonucleotides. In such case, typically several match probes areincluded with perfect complementarity to a given target mRNA together,optionally together with mismatch probes differing from the match probesare a known number of oligonucleotides (Lockhart, et al., NatureBiotechnology 14:1675-1680 (1996); and Lipschutz, et al., NatureGenetics Supplement 21: 20-24, 1999). Other arrays including full lengthcDNA sequences with perfect or near perfect complementarity to aparticular cDNA (Schena et al. (Science 270:467-470 (1995); and DeRisiet al. (Nature Genetics 14:457-460 (1996)). Such arrays can also includevarious control probes, such as a probe complementarity with a housekeeping gene likely to be expressed in most samples. Regardless of thespecifics of array design, an array contains one or more probes eitherperfectly complementary to a particular target mRNA or sufficientlycomplementarity to the target mRNA to distinguish it from other mRNAs inthe sample, and the presence of such a target mRNA can be determinedfrom the hybridization signal of such probes, optionally by comparisonwith mismatch or other control probes included in the array. Typically,the target bears a fluorescent label, in which case hybridizationintensity can be determined by, for example, a scanning confocalmicroscope in photon counting mode. Appropriate scanning devices aredescribed by e.g., U.S. Pat. Nos. 5,578,832, and 5,631,734. Theintensity of labeling of probes hybridizing to a particular mRNA or itsamplification product provides a raw measure of expression level.

In other methods, expression levels are determined by so-called “realtime amplification” methods also known as quantitative PCR or Taqman(see, e.g., U.S. Pat. No. 5,210,015 to Gelfand, U.S. Pat. No. 5,538,848to Livak, et al., and U.S. Pat. No. 5,863,736 to Haaland, as well asHeid, C. A., et al., Genome Research, 6:986-994 (1996); Gibson, U. E. M,et al., Genome Research 6:995-1001 (1996); Holland, P. M., et al., Proc.Natl. Acad. Sci. USA 88:7276-7280, (1991); and Livak, K. J., et al., PCRMethods and Applications 357-362 (1995)). The basis for this method ofmonitoring the formation of amplification product is to measurecontinuously PCR product accumulation using a dual-labeled fluorogenicoligonucleotide probe. The probe used in such assays is typically ashort (ca. 20-25 bases) polynucleotide that is labeled with twodifferent fluorescent dyes. The 5′ terminus of the probe is typicallyattached to a reporter dye and the 3′ terminus is attached to aquenching dye The probe is designed to have at least substantialsequence complementarity with a site on the target mRNA or nucleic acidderived from. Upstream and downstream PCR primers that bind to flankingregions of the locus are also added to the reaction mixture. When theprobe is intact, energy transfer between the two fluorophors occurs andthe quencher quenches emission from the reporter. During the extensionphase of PCR, the probe is cleaved by the 5′ nuclease activity of anucleic acid polymerase such as Taq polymerase, thereby releasing thereporter from the polynucleotide-quencher and resulting in an increaseof reporter emission intensity which can be measured by an appropriatedetector. The recorded values can then be used to calculate the increasein normalized reporter emission intensity on a continuous basis andultimately quantify the amount of the mRNA being amplified. mRNA levelscan also be measured without amplification by hybridization to a probe,for example, using a branched nucleic acid probe, such as a QuantiGene®Reagent System from Panomics.

In other methods, expression levels are determined by sequencingmethods. Sequencing methods may include: Next Generation sequencing,high-throughput sequencing, pyrosequencing, classic Sangar sequencingmethods, sequencing-by-ligation, sequencing by synthesis,sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression(Helicos), next generation sequencing, single molecule sequencing bysynthesis (SMSS) (Helicos), Ion Torrent Sequencing Machine (LifeTechnologies/Thermo-Fisher), massively-parallel sequencing, clonalsingle molecule Array (Solexa), shotgun sequencing, Maxim-Gilbertsequencing, primer walking, and any other sequencing methods known inthe art.

Alternatively or additionally, expression levels of genes can bedetermined at the protein level, meaning that levels of proteins encodedby the genes discussed above are measured. Several methods and devicesare well known for determining levels of proteins including immunoassayssuch as described in e.g., U.S. Pat. Nos. 6,143,576; 6,113,855;6,019,944; 5,985,579; 5,947,124; 5,939,272; 5,922,615; 5,885,527;5,851,776; 5,824,799; 5,679,526; 5,525,524; and 5,480,792. These assaysinclude various sandwich, competitive, or non-competitive assay formats,to generate a signal that is related to the presence or amount of anprotein analyte of interest. Any suitable immunoassay may be utilized,for example, lateral flow, enzyme-linked immunoassays (ELISA),radioimmunoassays (RIAs), competitive binding assays, and the like.Numerous formats for antibody arrays have been described proposedemploying antibodies. Such arrays typically include different antibodieshaving specificity for different proteins intended to be detected. Forexample, usually at least one hundred different antibodies are used todetect one hundred different protein targets, each antibody beingspecific for one target. Other ligands having specificity for aparticular protein target can also be used, such as the syntheticantibodies disclosed in WO/2008/048970. Other compounds with a desiredbinding specificity can be selected from random libraries of peptides orsmall molecules. U.S. Pat. No. 5,922,615 describes a device thatutilizes multiple discrete zones of immobilized antibodies on membranesto detect multiple target antigens in an array. U.S. Pat. Nos.5,458,852, 6,019,944, 6,143,576. Microtiter plates or automation can beused to facilitate detection of large numbers of different proteins.Protein levels can also be determined by mass spectrometry as describedin the examples.

The selection of genes for determination of expression levels depends onthe particular application. In general, the genes are selected from oneof the tables indicated above as appropriate for the application. Insome methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50,100, 150, or 200 (e.g., 10-100) genes shown in any of Table 7, 8, 9, 10,or 11 are determined. In some methods, expression levels of at least 2,3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes shown in Table 7 aredetermined. In some methods, expression levels of at least 2, 3, 4, 5,10, 20, 25, 50, 100, 150 or all genes in Table 8 are determined. In somemethods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100,150 or all genes shown in Table 9 are determined. In some methods,expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 orall genes shown in Table 10 are determined. In some methods, expressionlevels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genesshown in Table 11 are determined. In some methods, genes from differenttables are tested (e.g., at least 2, 3, 5, 10, 25, 50 or more genes fromeach of Table 7, Table 8, Table 9, Table 10, and/or Table 11). In somemethods, genes are selected such that genes from several differentpathways are represented (e.g., at least one gene from at least 2, 3, 5,or 10 pathways). The genes within a pathway tend to be expressed in acoordinated expression whereas genes from different pathways tend to beexpressed more independently. Thus, changes in expression based on theaggregate changes of genes from different pathways can have greaterstatistical significance than aggregate changes of genes within apathway. As noted above, expression levels can be measured at eithermRNA levels or protein levels.

Expression levels of the present genes and/or proteins can be combinedwith or without determination of expression levels of any other genes orproteins of interest (e.g., genes or proteins associated with rejectionof kidneys or other organs in WO 2007/104537, WO 2009/060035),Anglicheau et al., PNAS 106, 5330-5335 (2009)) and references, 16, 20,21, 22, 23, 25, 26, 37 and 39. In some methods, the genes in theexpression profiles to be measured do not include at least one or all ofthe genes known to be linked to graft rejection, e.g., genes describedin Halloran et al., Am. J. Transplant. 2013, 13(11):2865-74; andHalloran et al., Am. J. Transplant. 2013, 13(9):2352-63.

Regardless of the format adopted, the present methods can (but need not)be practiced by detection expression levels of a relatively small numberof genes or proteins compared with the whole genome level expressionanalysis described in the Examples. In some methods, the total number ofgenes whose expression levels are determined is less than 5000, 1000,500, 200, 100, 50, 25, 10, 5 or 3. In some methods, the total number ofgenes whose expression level is determined is 100-1500, 100-250,500-1500 or 750-1250. In some methods, the total number of proteinswhose expression levels are determined is less than 5000, 1000, 500,200, 100, 50, 25, 10, 5 or 3. In some methods, the total number ofproteins whose expression level is determined is 100-1500, 100-250,500-1500 or 750-1250. Correspondingly, when an array form is used fordetection of expression levels, the array includes probes or probes setsfor less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 genes. Thus,for example, an Affymetrix GeneChip® expression monitoring arraycontains a set of about 20-50 oligonucleotide probes (half match andhalf-mismatch) for monitoring each gene of interest. Such an arraydesign would include less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5or 3 such probes sets for detecting less than 5000, 1000, 500, 200, 100,50, 25, 10, 5 or 3 genes. By further example, an alternative arrayincluding one cDNA for each gene whose expression level is to bedetected would contain less than 5000, 1000, 500, 200, 100, 50, 25, 10,5 or 3 such cDNAs for analyzing less than 5000, 1000, 500, 200, 100, 50,25, 10, 5 or 3 genes. By further example, an array containing adifferent antibody for each protein to be detected would containing lessthan 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 different antibodiesfor analyzing less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3gene products.

Methods for detecting molecules (e.g., nucleic acids, proteins, etc.) ina subject who has received a transplant (e.g., organ transplant, tissuetransplant, stem cell transplant) in order to detect, diagnose, monitor,predict, or evaluate the status or outcome of the transplant aredescribed in this disclosure. In some cases, the molecules arecirculating molecules. In some cases, the molecules are expressed inblood cells. In some cases, the molecules are cell-free circulatingnucleic acids.

The methods, kits, and systems disclosed herein may be used to classifyone or more samples from one or more subjects. A sample may be anymaterial containing tissues, cells, nucleic acids, genes, genefragments, expression products, polypeptides, exosomes, gene expressionproducts, or gene expression product fragments of a subject to betested. Methods for determining sample suitability and/or adequacy areprovided. A sample may include but is not limited to, tissue, cells, orbiological material from cells or derived from cells of an individual.The sample may be a heterogeneous or homogeneous population of cells ortissues. In some cases, the sample is from a single patient. In somecases, the method comprises analyzing multiple samples at once, e.g.,via massively parallel sequencing.

The methods, kits, and systems disclosed herein may comprisespecifically detecting, profiling, or quantitating molecules (e.g.,nucleic acids, DNA, RNA, polypeptides, etc.) that are within thebiological samples. In some instances, genomic expression products,including RNA, or polypeptides, may be isolated from the biologicalsamples. In some cases, nucleic acids, DNA, RNA, polypeptides may beisolated from a cell-free source. In some cases, nucleic acids, DNA,RNA, polypeptides may be isolated from cells derived from the transplantrecipient.

The sample may be obtained using any method known to the art that canprovide a sample suitable for the analytical methods described herein.In some instances, the sample is obtained by an invasive procedureincluding but not limited to: biopsy, alveolar or pulmonary lavage, orneedle aspiration. The method of biopsy may include surgical biopsy,incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, orskin biopsy. The sample may be formalin fixed sections. The method ofneedle aspiration may further include fine needle aspiration, coreneedle biopsy, vacuum assisted biopsy, or large core biopsy. In someembodiments, multiple samples may be obtained by the methods herein toensure a sufficient amount of biological material. In some instances,the sample is not obtained by biopsy. In some instances, the sample isnot a kidney biopsy.

The total sample population may comprise samples obtained by needleaspiration, fine needle aspiration, core needle biopsy, vacuum assistedbiopsy, large core biopsy, incisional biopsy, excisional biopsy, punchbiopsy, shave biopsy, skin biopsy, or a combination thereof. In someembodiments, the samples are not obtained by biopsy. The percent of thetotal sample population that is obtained by biopsy may be greater thanabout 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, or 95%. The percent of the total samplepopulation that is obtained by biopsy may be greater than about 1%. Thepercent of the total sample population that is obtained by biopsy may begreater than about 5%. The percent of the total sample population thatis obtained by biopsy may be greater than about 10%.

V. Analysis of Expression Levels

Analysis of expression levels initially provides a measurement of theexpression level of each of several individual genes. The expressionlevel can be absolute in terms of a concentration of an expressionproduct, or relative in terms of a relative concentration of anexpression product of interest to another expression product in thesample. For example, relative expression levels of genes can beexpressed with respect to the expression level of a house-keeping genein the sample. Relative expression levels can also be determined bysimultaneously analyzing differentially labeled samples hybridized tothe same array. Expression levels can also be expressed in arbitraryunits, for example, related to signal intensity.

The individual expression levels, whether absolute or relative, can beconverted into values or other designations providing an indication ofpresence or risk of a kidney transplant rejection or injury bycomparison with one or more reference points. For different phenotypesof graft injuries (e.g., AR, ADNR, CAN; or TX), different gene sets aretypically used in the analysis. For example, chronic allograftnephropathy (CAN) can be determined with gene sets selected from Table 9or Table 7. Acute rejection (AR) and acute dysfunction no rejection(ADNR) can be determined with genes selected from Table 8 or Table 7.Acute rejection (AR) and chronic allograft nephropathy/interstitialfibrosis and tubular atrophy (IF/TA) can be determined with genesselected from Table 10 or Table 11.

For kidney transplant with each of the phenotypes noted above, thereference points can include a measure of an average or mean expressionlevel of a gene in subjects having had a kidney transplant with thespecific phenotype. The reference points can also include a scale ofvalues found in kidney transplant patients including patients havingthat phenotype. The reference points can also or alternatively include areference value in the subject before kidney transplant, or a referencevalue in a population of patients who have not undergone kidneytransplant. Such reference points can be expressed in terms of absoluteor relative concentrations of gene products as for measured values in asample.

For comparison between a measured expression level and referencelevel(s), the measured level sometimes needs to be normalized forcomparison with the reference level(s) or vice versa. The normalizationserves to eliminate or at least minimize changes in expression levelunrelated to the specific kidney transplant injury or phenotype (e.g.,from differences in overall health of the patient or samplepreparation). Normalization can be performed by determining what factoris needed to equalize a profile of expression levels measured fromdifferent genes in a sample with expression levels of these genes in aset of reference samples from which the reference levels weredetermined. Commercial software is available for performing suchnormalizations between different sets of expression levels.

Comparison of the measured expression level of a gene with one or moreof the above reference points provides a value (i.e., numerical) orother designation (e.g., symbol or word(s)) of presence orsusceptibility to a kidney transplant injury. In some methods, a binarysystem is used; that is a measured expression level of a gene isassigned a value or other designation indicating presence orsusceptibility to a kidney transplant injury or lack thereof withoutregard to degree. For example, the expression level can be assigned avalue of 1 to indicate presence or susceptibility to an injury and −1 toindicate absence or lack of susceptibility to the injury. Suchassignment can be based on whether the measured expression level iscloser to an average or mean level in kidney transplant patients havingor not having a specific injury phenotype. In other methods, a ternarysystem is used in which an expression level is assigned a value or otherdesignation indicating presence or susceptibility to a specific injuryphenotype or lack thereof or that the expression level is uninformative.Such assignment can be based on whether the expression level is closerto the average or mean level in kidney transplant patient undergoing thespecific injury, closer to an average or mean level in kidney transplantpatients lacking the injury or intermediate between such levels. Forexample, the expression level can be assigned a value of +1, −1 or 0depending on whether it is closer to the average or mean level inpatients undergoing the injury, is closer to the average or mean levelin patients not undergoing the injury or is intermediate. In othermethods, a particular expression level is assigned a value on a scale,where the upper level is a measure of the highest expression level foundin kidney transplant patients and the lowest level of the scale is ameasure of the lowest expression level found in kidney transplantpatients at a defined time point at which patients may be susceptible toa grant rejection or injury (e.g., one year post transplant).Preferably, such a scale is normalized scale (e.g., from 0-1) such thatthe same scale can be used for different genes. Optionally, the value ofa measured expression level on such a scale is indicated as beingpositive or negative depending on whether the upper level of the scaleassociates with presence or susceptibility to the injury or lackthereof. It does not matter whether a positive or negative sign is usedfor an injury phenotype or lack thereof as long as the usage isconsistent for different genes.

Values or other designation can also be assigned based on a change inexpression level of a gene relative to a previous measurement of theexpression level of gene in the same patient. Here as elsewhereexpression level of a gene can be measured at the protein or nucleicacid level. Such a change can be characterized as being toward, awayfrom or neutral with respect to average or mean expression levels of thegene in kidney transplant patients undergoing or not undergoing a grantrejection or injury. For example, a gene whose expression level changestoward an average or mean expression level in kidney transplant patientsundergoing a graft injury can be assigned a value of 1, and a gene whoseexpress level changes way from an average or mean expression level inkidney transplant patients undergoing the injury and toward an averageor mean expression level in kidney transplant patients not undergoingthe injury can be assigned a value −1. Of course, more sophisticatedsystems of assigning values are possible based on the magnitude ofchanges in expression of a gene in a patient.

Having determined values or other designations of expression levels ofindividual genes providing an indication of presence or susceptibilityto a kidney graft injury or lack thereof, the values or designations arecombined to provide an aggregate value for all of the genes beinganalyzed. If each gene is assigned a score of +1 if its expression levelindicates presence or susceptibility to a graft injury and −1 if itsexpression level indicates absence or lack of susceptibility to theinjury and optionally zero if uninformative, the different values can becombined by addition. The same approach can be used if each gene isassigned a value on the same normalized scale and assigned as beingpositive or negative depending whether the upper point of the scale isassociate with presence or susceptibility to a specific kidney grantinjury or lack thereof. In some cases, the signal intensity for eachgene is obtained and used to compute a score. The score may be obtainedby adding up the values for the upregulated genes to obtain anupregulated gene value and adding up the values of the downregulatedgenes to obtain a downregulated gene value; the downregulated gene valuemay be compared with the upregulated value (e.g., by calculating aratio) to determine the score. Other methods of combining values forindividual markers of disease into a composite value that can be used asa single marker are described in US20040126767 and WO/2004/059293. Insome cases, the score may be used to evaluate severity of a transplantcondition, such as by comparing the score with a score normallyassociated with kidney graft injury. In some cases, the score may beused to monitor a subject transplant recipient over time. In such case,scores at a plurality of timepoints maybe compared in order to assessthe relative condition of the subject. For example, if the subject'sscore rises over time, that may indicate that the subject has kidneygraft injury and that his or her condition is worsening over time.

Sample Data

The data pertaining to the sample may be compared to data pertaining toone or more control samples, which may be samples from the same patientat different times. In some cases, the one or more control samples maycomprise one or more samples from healthy subjects, unhealthy subjects,or a combination thereof. The one or more control samples may compriseone or more samples from healthy subjects, subjects suffering fromtransplant dysfunction with no rejection, subjects suffering fromtransplant rejection, or a combination thereof. The healthy subjects maybe subjects with normal transplant function. The data pertaining to thesample may be sequentially compared to two or more classes of samples.The data pertaining to the sample may be sequentially compared to threeor more classes of samples. The classes of samples may comprise controlsamples classified as being from subjects with normal transplantfunction, control samples classified as being from subjects sufferingfrom transplant dysfunction with no rejection, control samplesclassified as being from subjects suffering from transplant rejection,or a combination thereof

Classifiers

The methods include using a trained classifier or algorithm to analyzesample data, particularly to detect subAR. In some instances, theexpression levels from sample are used to develop or train an algorithmor classifier provided herein. In some instances, gene expression levelsare measured in a sample from a transplant recipient (or a healthy ortransplant excellent control) and a classifier or algorithm (e.g.,trained algorithm) is applied to the resulting data in order to detect,predict, monitor, or estimate the risk of a transplant condition (e.g.,subAR).

Training of multi-dimensional classifiers (e.g., algorithms) may beperformed using numerous samples. For example, training of themulti-dimensional classifier may be performed using at least about 10,20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200 or more samples. In some cases, training of themulti-dimensional classifier may be performed using at least about 200,210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500 ormore samples. In some cases, training of the multi-dimensionalclassifier may be performed using at least about 525, 550, 600, 650,700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600,1700, 1800, 2000 or more samples.

Further disclosed herein are classifier sets and methods of producingone or more classifier sets. The classifier set may comprise one or moregenes, particularly genes from Tables 7, 8, or 9. In some cases, theclassifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 50, 100, 150, 200, 300 or more genes fromTables 7, 8, 9, 10, or 11. Disclosed herein is the use of aclassification system comprises one or more classifiers. In someinstances, the classifier is a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, or 10-wayclassifier. In some instances, the classifier is a 15-, 20-, 25-, 30-,35-, 40-, 45-, 50-, 55-, 60-, 65-, 70-, 75-, 80-, 85-, 90-, 95-, or100-way classifier. In some preferred embodiments, the classifier is athree-way classifier. In some embodiments, the classifier is a four-wayclassifier.

A two-way classifier may classify a sample from a subject into one oftwo classes. In some instances, a two-way classifier may classify asample from an organ transplant recipient into one of two classescomprising subAR and normal transplant function (TX). In some instances,a three-way classifier may classify a sample from a subject into one ofthree classes. A three-way classifier may classify a sample from anorgan transplant recipient into one of three classes comprising AR,subAR, and TX. In some cases, the classifier may work by applying two ormore classifiers sequentially. For example, the first classifier mayclassify AR+subAR and TX, which results in a set of samples that areclassified either as (1) TX or (2) AR or subAR. In some cases, a secondclassifier capable of distinguishing between AR and subAR is applied tothe samples classified as having AR or subAR in order to detect thesubAR samples.

Classifiers and/or classifier probe sets may be used to either rule-inor rule-out a sample as healthy. For example, a classifier may be usedto classify a sample as being from a healthy subject. Alternatively, aclassifier may be used to classify a sample as being from an unhealthysubject. Alternatively, or additionally, classifiers may be used toeither rule-in or rule-out a sample as transplant rejection. Forexample, a classifier may be used to classify a sample as being from asubject suffering from a transplant rejection. In another example, aclassifier may be used to classify a sample as being from a subject thatis not suffering from a transplant rejection. Classifiers may be used toeither rule-in or rule-out a sample as transplant dysfunction with norejection. For example, a classifier may be used to classify a sample asbeing from a subject with subAR. In another example, a classifier may beused to classify a sample as not being from a subject suffering fromtransplant dysfunction with no rejection.

The data pertaining to the sample may be compared to data pertaining toone or more control samples, which may be samples from the same patientat different times. In some cases, the one or more control samples maycomprise one or more samples from healthy subjects, unhealthy subjects,or a combination thereof. The one or more control samples may compriseone or more samples from healthy subjects, subjects suffering fromtransplant dysfunction with no rejection, subjects suffering fromtransplant rejection, or a combination thereof. The healthy subjects maybe subjects with normal transplant function. The data pertaining to thesample may be sequentially compared to two or more classes of samples.The data pertaining to the sample may be sequentially compared to threeor more classes of samples. The classes of samples may comprise controlsamples classified as being from subjects with normal transplantfunction, control samples classified as being from subjects sufferingfrom transplant dysfunction with no rejection, control samplesclassified as being from subjects suffering from transplant rejection,or a combination thereof.

The methods, kits, and systems disclosed herein may comprise one or morealgorithms or uses thereof. The one or more algorithms may be used toclassify one or more samples from one or more subjects. The one or morealgorithms may be applied to data from one or more samples. The data maycomprise gene expression data. The data may comprise sequencing data.The data may comprise array hybridization data.

The methods disclosed herein may comprise assigning a classification toone or more samples from one or more subjects. Assigning theclassification to the sample may comprise applying an algorithm to theexpression level. In some cases, the gene expression levels are inputtedto a trained algorithm for classifying the sample as one of theconditions comprising subAR, AR, TX, subAR+AR, or other condition.

The algorithm may provide a record of its output including aclassification of a sample and/or a confidence level. In some instances,the output of the algorithm can be the possibility of the subject ofhaving a condition, such as subAR. In some instances, the output of thealgorithm can be the risk of the subject of having a condition, such assubAR. In some instances, the output of the algorithm can be thepossibility of the subject of developing into a condition in the future,such as subAR.

The algorithm may be a trained algorithm. The algorithm may comprise alinear classifier. The linear classifier may comprise one or more lineardiscriminant analysis, Fisher's linear discriminant, Naïve Bayesclassifier, Logistic regression, Perceptron, Support vector machine, ora combination thereof. The linear classifier may be a Support vectormachine (SVM) algorithm.

The algorithm may comprise one or more linear discriminant analysis(LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel)Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis(DLDA), Golub Classifier, Parzen-based, (kernel) Fisher DiscriminantClassifier, k-nearest neighbor, Iterative RELIEF, Classification Tree,Maximum Likelihood Classifier, Random Forest, Nearest Centroid,Prediction Analysis of Microarrays (PAM), k-medians clustering, FuzzyC-Means Clustering, Gaussian mixture models, or a combination thereof.The algorithm may comprise a Diagonal Linear Discriminant Analysis(DLDA) algorithm. The algorithm may comprise a Nearest Centroidalgorithm. The algorithm may comprise a Random Forest algorithm. Thealgorithm may comprise a Prediction Analysis of Microarrays (PAM)algorithm.

The methods disclosed herein may comprise use of one or more classifierequations. Classifying the sample may comprise a classifier equation.The classifier equation may be Equation 1:

${{\delta_{k}( x^{*} )} = {{\sum\limits_{i = 1}^{p}\; \frac{( {x_{i}^{*} - {\overset{\_}{x}}_{ik}^{\prime}} )^{2}}{( {s_{i} + s_{0}} )^{2}}} - {2\mspace{14mu} \log \mspace{14mu} \pi_{k}}}},$

wherein:

k is a number of possible classes;

δ_(k) may be the discriminant score for class k;

x_(i)* represents the expression level of gene i;

x* represents a vector of expression levels for all p genes to be usedfor classification drawn from the sample to be classified;

x _(k)′ may be a shrunken centroid calculated from a training data and ashrinkage factor;

x _(ik)′ may be a component of x _(k)′ corresponding to gene i;

s_(i) is a pooled within-class standard deviation for gene i in thetraining data;

s₀ is a specified positive constant; and

π_(k) represents a prior probability of a sample belonging to class k.

Assigning the classification may comprise calculating a classprobability. Calculating the class probability {circumflex over(p)}_(k)(x*) may be calculated by Equation 2:

${{\hat{p}}_{k}( x^{*} )} = {\frac{e^{{- \frac{1}{2}}{\delta_{k}{(x^{*})}}}}{\sum\limits_{l = 1}^{K}\; e^{{- \frac{1}{2}}{\delta_{l}{(x^{*})}}}}.}$

Assigning the classification may comprise a classification rule. Theclassification rule C(x*) may be expressed by Equation 3:

${C( x^{*} )} = {\underset{k \in {\{{1,K}\}}}{\arg \mspace{14mu} \max}\mspace{14mu} {{{\hat{p}}_{k}( x^{*} )}.}}$

VI. Diagnosis, Prognosis and Monitoring

The above described methods can provide a value or other designation fora patient which indicates whether the aggregate measured expressionlevels in a patient is more like kidney transplant patients with one ofthe graft injury phenotypes noted above (e.g., AR, ADNR, CAN, or TX).Such a value provides an indication that the patient either has or is atenhanced risk of developing a specific graft injury, or conversely doesnot have or is at reduced risk of having that specific graft injuryphenotype. Risk is a relative term in which risk of one patient iscompared with risk of other patients either qualitatively orquantitatively. For example, the value of one patient can be comparedwith a scale of values for a population of patients having undergonekidney transplant to determine whether the patient's risk relative tothat of other patients. In general, diagnosis is the determination ofthe present condition of a patient (e.g., presence or absence of a graftinjury) and prognosis is developing future course of the patient (e.g.,risk of developing kidney transplant rejection or injury in the futureor likelihood of improvement in response to treatment); however, theanalyses contemplated by these terms may overlap or even be the same.For example, the present methods alone do not necessarily distinguishbetween presence and enhanced risk of a kidney transplant injury.However, these possibilities can be distinguished by additional testing.

If a patient is indicated as having or being at enhanced risk of akidney transplant injury, the physician can subject the patient toadditional testing including performing a kidney biopsy examination, orperforming other analyses such as creatinine, BUN or glomerularfiltration rate at increased frequency. Additionally or alternatively,the physician can change the treatment regime being administered to thepatient. This includes administration of steroid boluses and theaddition of other drugs to the maintenance therapy, or theadministration of antilymphocyte antibodies in case of resistance to theprimary line of therapy. In some embodiments, the change in treatmentregime can include administering an additional or different drug, oradministering a higher dosage or frequency of a drug already beingadministered to the patient. Many different drugs are available fortreating rejection, such as immunosuppressive drugs used to treattransplant rejection calcineurin inhibitors (e.g., cyclosporine,tacrolimus), mTOR inhibitors (e.g., sirolimus and everolimus),anti-proliferatives (e.g., azathioprine, mycophenolic acid),corticosteroids (e.g., prednisolone and hydrocortisone) and antibodies(e.g., basiliximab, daclizumab, Orthoclone, anti-thymocyte globulin andanti-lymphocyte globulin). In the case of HCV recurrence, the patientsmay be additionally administered drugs to counter the viral infection,e.g., interferons, ribavirin, and protease inhibitors. Conversely, ifthe value or other designation of aggregate expression levels of apatient indicates the patient does not have or is at reduced risk ofgraft injury, the physician need not order further diagnosticprocedures, particularly not invasive ones such as biopsy. Further, thephysician can continue an existing treatment regime, or even decreasethe dose or frequency of an administered drug.

In some methods, expression levels are determined at intervals in aparticular patient (i.e., monitoring). Such methods can provide a seriesof values changing over time indicating whether the aggregate expressionlevels in a particular patient are more like the expression levels inpatients undergoing a specific kidney transplant rejection/injury or notundergoing the rejection/injury. Movement in value toward or away fromthe graft injury can provide an indication whether an existingimmunosuppressive regime is working, whether the immunosuppressiveregime should be changed or whether a biopsy or increased monitoring byother markers rate should be performed.

VII. Drug Screening

The expression profiles associated with a kidney transplantrejection/injury or lack thereof provided by the invention are useful inscreening drugs, either in clinical trials or in animal models of theinjury. A clinical trial can be performed on a drug in similar fashionto the monitoring of an individual patient described above, except thatdrug is administered in parallel to a population of kidney transplantpatients, usually in comparison with a control population administered aplacebo.

The changes in expression levels of genes can be analyzed in individualpatients and across a treated or control population. Analysis at thelevel of an individual patient provides an indication of the overallstatus of the patient at the end of the trial (i.e., whether geneexpression profile indicates presence or enhanced susceptibility to akidney transplant rejection/injury) and/or an indication whether thatprofile has changed toward or away from such indication in the course ofthe trial. Results for individual patients can be aggregated for apopulation allowing comparison between treated and control population.Mannon et al., Kidney International (1999) 55, 1935-1944. In this case,the expression levels of genes detected are the species variants orhomologs of the human genes referenced above in whatever species ofnon-human animal on which tests are being conducted. Although theaverage or mean expression levels of human genes determined in humankidney transplant patients undergoing or not undergoing a specifictransplant rejection/injury are not necessarily directly comparable tothose of homolog genes in an animal model, the human values cannevertheless be used to provide an indication whether a change inexpression level of a non-human homolog is in a direction toward or awayfrom an injury or susceptibility thereto. The expression profile ofindividual animals in a trial can provide an indication of the status ofthe animal at the end of the trial with respect to presence orsusceptibility to the injury and/or change in such status during thetrial. Results from individual animals can be aggregated across apopulation and treated and control populations compared. Average changesin the expression levels of genes can then be compared between the twopopulations.

VIII. Computer Implemented Methods

Expression levels can be analyzed and associated with status of asubject (e.g., presence or susceptibility to a kidney transplant injury)in a digital computer. Optionally, such a computer is directly linked toa scanner or the like receiving experimentally determined signalsrelated to expression levels. Alternatively, expression levels can beinput by other means. The computer can be programmed to convert rawsignals into expression levels (absolute or relative), compare measuredexpression levels with one or more reference expression levels, or ascale of such values, as described above. The computer can also beprogrammed to assign values or other designations to expression levelsbased on the comparison with one or more reference expression levels,and to aggregate such values or designations for multiple genes in anexpression profile. The computer can also be programmed to output avalue or other designation providing an indication of presence orsusceptibility to a rejection as well as any of the raw or intermediatedata used in determining such a value or designation.

A typical computer (see U.S. Pat. No. 6,785,613 FIGS. 4 and 5) includesa bus which interconnects major subsystems such as a central processor,a system memory, an input/output controller, an external device such asa printer via a parallel port, a display screen via a display adapter, aserial port, a keyboard, a fixed disk drive and a floppy disk driveoperative to receive a floppy disk. Many other devices can be connectedsuch as a scanner via I/O controller, a mouse connected to serial portor a network interface. The computer contains computer readable mediaholding codes to allow the computer to perform a variety of functions.These functions include controlling automated apparatus, receiving inputand delivering output as described above. The automated apparatus caninclude a robotic arm for delivering reagents for determining expressionlevels, as well as small vessels, e.g., microtiter wells for performingthe expression analysis.

The methods, systems, kits and compositions provided herein may also becapable of generating and transmitting results through a computernetwork. As shown in FIG. 2, a sample (220) is first collected from asubject (e.g. transplant recipient, 210). The sample is assayed (230)and gene expression products are generated. A computer system (240) isused in analyzing the data and making classification of the sample. Theresult is capable of being transmitted to different types of end usersvia a computer network (250). In some instances, the subject (e.g.patient) may be able to access the result by using a standalone softwareand/or a web-based application on a local computer capable of accessingthe internet (260). In some instances, the result can be accessed via amobile application (270) provided to a mobile digital processing device(e.g. mobile phone, tablet, etc.). In some instances, the result may beaccessed by physicians and help them identify and track conditions oftheir patients (280). In some instances, the result may be used forother purposes (290) such as education and research.

EXAMPLES

The following examples are offered to illustrate, but not to limit thepresent invention.

Example 1. Differentially Expressed Genes Associated with KidneyTransplant Rejections

This Example describes global analysis of gene expressions in kidneytransplant patients with different types of rejections or injuries.

A total of biopsy-documented 274 kidney biopsy samples from theTransplant Genomics Collaborative Group (TGCG) were processed on theAffymetrix HG-U133 PM only peg microarrays. The 274 samples that wereanalyzed comprised of 4 different phenotypes: Acute Rejection (AR;n=75); Acute Dysfunction No Rejection (ADNR; n=39); Chronic AllograftNephropathy (CAN; n=61); and Transplant Excellent (TX; n=99).

Signal Filters: To eliminate low expressed signals we used a signalfilter cut-off that was data driven, and expression signals <Log 2 4.23in all samples were eliminated leaving us with 48882 probe sets from atotal of 54721 probe sets.

4-Way AR/ADNR/CAN/TX classifier: We first did a 4 way comparison of theAR, ADNR, CAN and TX samples. The samples comprised of four differentclasses a 4-way ANOVA analysis yielded more than 10,000 differentiallyexpressed genes even at a stringent p value cut-off of <0.001. Since wewere trying to discover a signature that could differentiate these fourclasses we used only the top 200 differentially expressed probe sets tobuild predictive models. We ran the Nearest Centroid (NC) algorithm tobuild the predictive models. When we used the top 200 differentiallyexpressed probe sets between all four phenotypes, the best predictormodel was based on 199 probe sets.

Nearest Centroid (NC) classification takes the gene expression profileof a new sample, and compares it to each of the existing classcentroids. The class whose centroid that it is closest to, in squareddistance, is the predicted class for that new sample. It also providesthe centroid distances for each sample to each of the possiblephenotypes being tested. In other words, in a 2-way classifier like ARvs. TX, the tool provides the “best” classification and provides thecentroid distances to the two possible outcomes: TX and AR.

We observed in multiple datasets that there are 4 classes of predictionsmade. First, are correctly classified as TX by both biopsy and NC.Second, are correctly classified as AR by both biopsy and NC. Third, aretruly misclassified samples. In other words, the biopsy says one thingand the molecular profile another. In these cases, the centroiddistances for the given classifications are dramatically different,making the molecular classification very straightforward and simply notconsistent with the biopsy phenotype assigned. Whether this is becausethe gold standard biopsy classification is wrong or the molecularclassification is wrong is impossible to know at this point.

However, there is a fourth class that we call “mixed” classifications.In these cases supposedly “misclassified” samples by molecular profileshow a nearest centroid distance that is not very different whencompared to that of the “correct” classification based on the biopsy. Inother words, the nearest centroid distances of most of thesemisclassified “mixed” samples are actually very close to the correctbiopsy classification. However, because NC has no rules set to deal withthe mixed situation it simply calls the sample by the nominally highercentroid distance.

The fact is that most standard implementations of class predictionalgorithms currently available treat all classes as dichotomousvariables (yes/no diagnostically). They are not designed to deal withthe reality of medicine that molecular phenotypes of clinical samplescan actually represent a continuous range of molecular scores based onthe expression signal intensities with complex implications for thediagnoses. Thus, “mixed” cases where the centroid distances are onlyslightly higher for TX than AR is still classified as a TX, even if theAR distances are only slightly less. In this case, where there is amixture of TX and AR by expression, it is obvious that the case isactually an AR for a transplant clinician, not a TX. Perhaps just amilder form of AR and this is the reason for using thresholding.

Thus, we set a threshold for the centroid distances. The threshold isdriven by the data. The threshold equals the mean difference NC providesin centroid distances for the two possible classifications (i.e. AR vs.TX) for all correctly classified samples in the data set (e.g. classes 1and 2 of the 4 possible outcomes of classification). This means that forthe “mixed” class of samples, if a biopsy-documented sample wasmisclassified by molecular profiling, but the misclassification waswithin the range of the mean calculated centroid distances of the trueclassifications in the rest of the data, then that sample would not beconsidered as a misclassified sample.

Table 1 shows the performance of the 4 way AR, ADNR, CAN, TX NCclassifier using such a data driven threshold. So, using the top 200differentially expressed probesets from a 4-way AR, ADNR, CAN and TXANOVA with a Nearest Centroid classifier, we are able to molecularlyclassify the 4 phenotypes at 97% accuracy. Smaller classifier sets didnot afford any significant increase in the predictive accuracies. Tovalidate this data we applied this classification to an externallycollected data set. These were samples collected at the University ofSao Paolo in Brazil. A total of 80 biopsy-documented kidney biopsysamples were processed on the same Affymetrix HG-U133 PM only pegmicroarrays. These 80 samples that were analyzed comprised of the same 4different phenotypes: AR (n=23); ADNR (n=11); CAN (n=29); and TX (n=17).

We performed the classification based on the “locked” NC predictor(meaning that none of the thresholding parameters were changed. Table 2shows the performance of our locked 4 way AR, ADNR, CAN, TX NCclassifier in the Brazilian cohort. So, using the top 200 differentiallyexpressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a“locked” Nearest Centroid classifier we are able to molecularly classifythe 4 phenotypes with similar accuracy in an independently andexternally collected validation set. This validates our molecularclassifier of the biopsy on an independent external data set. It alsodemonstrates that the classifier is not subject to influence based onsignificant racial differences represented in the Brazilian population.

3-Way AR/ADNR/TX classifier: Similarly, we did a 3 way comparison of theAR, ADNR and TX samples since these are the most common phenotypesencountered during the early post-transplant period with CAN usuallybeing a late manifestation of graft injury which is progressive. Thesamples comprised of these 3 different classes, and a 4-way ANOVAanalysis again yielded more than 10,000 differentially expressed genes,so we used only the top 200 differentially expressed probe sets to buildpredictive models. We ran the Nearest Centroid (NC) algorithm to buildthe predictive models. When we used the top 200 differentially expressedprobe sets between all four phenotypes the best predictor model wasbased on 197 probe sets.

Table 3 shows the performance of the 3 way AR, ADNR, TX NC classifierwith which we are able to molecularly classify the 3 phenotypes at 98%accuracy in the TGCG cohort. Similarly the locked 3 way classifierperforms equally well on the Brazilian cohort with 98% accuracy (Table4). Therefore, our 3 way classifier also validates on the external dataset.

2-Way CAN/TX classifier: Finally we also did a 2 way comparison of theCAN and TX samples. The samples comprised of these 2 classes with anANOVA analysis again yielded 11,000 differentially expressed genes, sowe used only the top 200 differentially expressed probe sets to buildpredictive models. We ran the Nearest Centroid (NC) algorithm to buildthe predictive models. When we used the top 200 differentially expressedprobe sets the best predictor model was based on all 200 probe sets.Table 5 shows the performance of the 2 way CAN, TX NC classifier withwhich we are able to molecularly classify the 4 phenotypes at 97%accuracy in the TGCG cohort. This locked classifier performs equallywell on the Brazilian cohort with 95% accuracy (Table 6). Again we showthat our 2 way CAN, TX classifier also validates on the external dataset.

In another example of the 2-way classifier similar to the 2-way CAN/TXclassifier described earlier, Table 10 shows the top 400 probe sets of2-Way Classifier AR vs. TX and Table 11 shows the top 400 probe sets of2-Way Classifier CAN/IFTA vs. TX.

Clinical Applications

The methods, compositions, systems and kits provided herein can be usedto detect, diagnose, predict or monitor a condition of a transplantrecipient. In some instances, the methods, compositions, systems andkits described herein provide information to a medical practitioner thatcan be useful in making a therapeutic decision. Therapeutic decisionsmay include decisions to: continue with a particular therapy, modify aparticular therapy, alter the dosage of a particular therapy, stop orterminate a particular therapy, altering the frequency of a therapy,introduce a new therapy, introduce a new therapy to be used incombination with a current therapy, or any combination of the above

Detecting/Diagnosing a Condition of a Transplant Recipient

The methods, compositions, systems and kits provided herein areparticularly useful for detecting or diagnosing a condition of atransplant recipient such as a condition the transplant recipient has atthe time of testing. Exemplary conditions that can be detected ordiagnosed with the present methods include organ transplant rejection,acute rejection (AR), chronic rejection, Acute Dysfunction with NoRejection (ADNR), normal transplant function (TX) and/or Sub-ClinicalAcute Rejection (SubAR). The methods provided herein are particularlyuseful for transplant recipients who have received a kidney transplant.Exemplary conditions that can be detected or diagnosed in such kidneytransplant recipients include: AR, chronic allograft nephropathy (CAN),ADNR, SubAR, IF/TA, and TX.

The diagnosis or detection of condition of a transplant recipient may beparticularly useful in limiting the number of invasive diagnosticinterventions that are administered to the patient. For example, themethods provided herein may limit or eliminate the need for a transplantrecipient (e.g., kidney transplant recipient) to receive a biopsy (e.g.,kidney biopsies) or to receive multiple biopsies. In some instances, themethods provided herein may also help interpreting a biopsy result,especially when the biopsy result is inconclusive.

In a further embodiment, the methods provided herein can be used aloneor in combination with other standard diagnosis methods currently usedto detect or diagnose a condition of a transplant recipient, such as butnot limited to results of biopsy analysis for kidney allograftrejection, results of histopathology of the biopsy sample, serumcreatinine level, creatinine clearance, ultrasound, radiological imagingresults for the kidney, urinalysis results, elevated levels ofinflammatory molecules such as neopterin, and lymphokines, elevatedplasma interleukin (IL)-1 in azathioprine-treated patients, elevatedIL-2 in cyclosporine-treated patients, elevated IL-6 in serum and urine,intrarenal expression of cytotoxic molecules (granzyme B and perforin)and immunoregulatory cytokines (IL-2, -4, -10, interferon gamma andtransforming growth factor-b1).

The methods provided herein are useful for distinguishing between two ormore conditions or disorders (e.g., AR vs ADNR, SubAR vs ADNR, etc.). Insome instances, the methods are used to determine whether a transplantrecipient has AR, ADNR or TX. In some instances, the methods are used todetermine whether a transplant recipient has AR, ADNR, SubAR and/or TX,or any subset or combination thereof. In some instances, the methods areused to determine whether a transplant recipient has AR, ADNR, SubAR,TX, HCV, or any subset or combination thereof. As previously described,elevated serum creatinine levels from baseline levels in kidneytransplant recipients may be indicative of AR or ADNR. In preferredembodiments, the methods provided herein are used to distinguish AR fromADNR in a kidney transplant recipient. In some preferred embodiments,the methods provided herein are used to distinguish AR from ADNR in aliver transplant recipient. In some instances, the methods are used todetermine whether a transplant recipient has AR, ADNR, SubAR, TX, acutetransplant dysfunction, transplant dysfunction, transplant dysfunctionwith no rejection, or any subset or combination thereof. In someinstances, the methods provided herein are used to distinguish AR fromADNR from CAN a kidney transplant recipient.

Predicting a Condition of a Transplant Recipient

In some embodiments, the methods provided herein can predict AR prior toactual onset of the conditions. In some instances, the methods providedherein can predict AR, IFTA, CAN, ADNR, SubAR or other disorders in atransplant recipient at least 1 day, 5 days, 10 days, 30 days, 50 daysor 100 days prior to onset. In other instances, the methods providedherein can predict AR, IFTA, CAN, ADNR, SubAR or other disorders in atransplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31days prior to onset. In other instances, the methods provided herein canpredict AR, IFTA, CAN, ADNR, SubAR or other disorders in a transplantrecipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 months prior toonset.

Monitoring a Condition of a Transplant Recipient

Provided herein are methods, systems, kits and compositions formonitoring a condition of a transplant recipient. Often, the monitoringis conducted by serial testing, such as serial non-invasive tests,serial minimally-invasive tests (e.g., blood draws), serial invasivetests (biopsies), or some combination thereof. Preferably, themonitoring is conducted by administering serial non-invasive tests orserial minimally-invasive tests (e.g., blood draws).

In some instances, the transplant recipient is monitored as needed usingthe methods described herein. Alternatively the transplant recipient maybe monitored hourly, daily, weekly, monthly, yearly or at anypre-specified intervals. In some instances, the transplant recipient ismonitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hours. In some instancesthe transplant recipient is monitored at least once every 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30 or 31 days. In some instances, the transplantrecipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12 months. In some instances, the transplant recipient ismonitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years orlonger, for the lifetime of the patient and the graft.

In some instances, gene expression levels in the patients can bemeasured, for example, within, one month, three months, six months, oneyear, two years, five years or ten years after a transplant. In somemethods, gene expression levels are determined at regular intervals,e.g., every 3 months, 6 months or every year post-transplant, eitherindefinitely, or until evidence of a condition is observed, in whichcase the frequency of monitoring is sometimes increased. In somemethods, baseline values of expression levels are determined in asubject before a transplant in combination with determining expressionlevels at one or more time points thereafter.

The results of diagnosing, predicting, or monitoring a condition of atransplant recipient may be useful for informing a therapeutic decisionsuch as determining or monitoring a therapeutic regimen. In someinstances, determining a therapeutic regimen may comprise administeringa therapeutic drug. In some instances, determining a therapeutic regimencomprises modifying, continuing, initiating or stopping a therapeuticregimen. In some instances, determining a therapeutic regimen comprisestreating the disease or condition. In some instances, the therapy is animmunosuppressive therapy. In some instances, the therapy is anantimicrobial therapy. In other instances, diagnosing, predicting, ormonitoring a disease or condition comprises determining the efficacy ofa therapeutic regimen or determining drug resistance to the therapeuticregimen.

Modifying the therapeutic regimen may comprise terminating a therapy.Modifying the therapeutic regimen may comprise altering a dosage of atherapy. Modifying the therapeutic regimen may comprise altering afrequency of a therapy. Modifying the therapeutic regimen may compriseadministering a different therapy. In some instances, the results ofdiagnosing, predicting, or monitoring a condition of a transplantrecipient may be useful for informing a therapeutic decision such asremoval of the transplant. In some instances, the removal of thetransplant can be an immediate removal. In other instances, thetherapeutic decision can be a retransplant. Other examples oftherapeutic regimen can include a blood transfusion in instances wherethe transplant recipient is refractory to immunosuppressive or antibodytherapy.

Examples of therapeutic regimen can include administering compounds oragents that are e.g., compounds or agents having immunosuppressiveproperties (e.g., a calcineurin inhibitor, cyclosporine A or FK 506); amTOR inhibitor (e.g., rapamycin, 40-O-(2-hydroxyethyl)-rapamycin,CCI779, ABT578, AP23573, biolimus-7 or biolimus-9); an ascomycin havingimmuno-suppressive properties (e.g., ABT-281, ASM981, etc.);corticosteroids; cyclophosphamide; azathioprene; methotrexate;leflunomide; mizoribine; mycophenolic acid or salt; mycophenolatemofetil; 15-deoxyspergualine or an immunosuppressive homologue, analogueor derivative thereof; a PKC inhibitor (e.g., as disclosed in WO02/38561 or WO 03/82859); a JAK3 kinase inhibitor (e.g.,N-benzyl-3,4-dihydroxy-benzylidene-cyanoacetamidea-cyano-(3,4-dihydroxy)-]N-benzylcinnamamide (Tyrphostin AG 490),prodigiosin 25-C(PNU156804),[4-(4′-hydroxyphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P131),[4-(3′-bromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline](WHI-P154),[4-(3′,5′-dibromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline]WHI-P97, KRX-211,3-{(3R,4R)-4-methyl-3-[methyl-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-amino]-pi-peridin-1-yl}-3-oxo-propionitrile, in free form or in a pharmaceuticallyacceptable salt form, e.g., mono-citrate (also called CP-690,550), or acompound as disclosed in WO 04/052359 or WO 05/066156); a SIP receptoragonist or modulator (e.g., FTY720 optionally phosphorylated or ananalog thereof, e.g.,2-amino-2-[4-(3-benzyloxyphenylthio)-2-chlorophenyl]ethyl-1,3-propanedioloptionally phosphorylated or1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylicacid or its pharmaceutically acceptable salts); immunosuppressivemonoclonal antibodies (e.g., monoclonal antibodies to leukocytereceptors, e.g., MHC, CD2, CD3, CD4, CD7, CD8, CD25, CD28, CD40, CD45,CD52, CD58, CD80, CD86 or their ligands); other immunomodulatorycompounds (e.g., a recombinant binding molecule having at least aportion of the extracellular domain of CTLA4 or a mutant thereof, e.g.,an at least extracellular portion of CTLA4 or a mutant thereof joined toa non-CTLA4 protein sequence, e.g., CTLA4Ig (for ex. designated ATCC68629) or a mutant thereof, e.g., LEA29Y); adhesion molecule inhibitors(e.g., LFA-1 antagonists, ICAM-1 or -3 antagonists, VCAM-4 antagonistsor VLA-4 antagonists). These compounds or agents may also be used aloneor in combination. Immunosuppressive protocols can differ in differentclinical settings. In some instances, in AR, the first-line treatment ispulse methylprednisolone, 500 to 1000 mg, given intravenously daily for3 to 5 days. In some instances, if this treatment fails, than OKT3 orpolyclonal anti-T cell antibodies will be considered. In otherinstances, if the transplant recipient is still experiencing AR,antithymocyte globulin (ATG) may be used.

Kidney Transplants

The methods, compositions, systems and kits provided herein areparticularly useful for detecting or diagnosing a condition of a kidneytransplant. Kidney transplantation may be needed when a subject issuffering from kidney failure, wherein the kidney failure may be causedby hypertension, diabetes melitus, kidney stone, inherited kidneydisease, inflammatory disease of the nephrons and glomeruli, sideeffects of drug therapy for other diseases, etc. Kidney transplantationmay also be needed by a subject suffering from dysfunction or rejectionof a transplanted kidney.

Kidney function may be assessed by one or more clinical and/orlaboratory tests such as complete blood count (CBC), serum electrolytestests (including sodium, potassium, chloride, bicarbonate, calcium, andphosphorus), blood urea test, blood nitrogen test, serum creatininetest, urine electrolytes tests, urine creatinine test, urine proteintest, urine fractional excretion of sodium (FENA) test, glomerularfiltration rate (GFR) test. Kidney function may also be assessed by arenal biopsy. Kidney function may also be assessed by one or more geneexpression tests. The methods, compositions, systems and kits providedherein may be used in combination with one or more of the kidney testsmentioned herein. The methods, compositions, systems and kits providedherein may be used before or after a kidney transplant. In someinstances, the method may be used in combination with complete bloodcount. In some instances, the method may be used in combination withserum electrolytes (including sodium, potassium, chloride, bicarbonate,calcium, and phosphorus). In some instances, the method may be used incombination with blood urea test. In some instances, the method may beused in combination with blood nitrogen test. In some instances, themethod may be used in combination with a serum creatinine test. In someinstances, the method may be used in combination with urine electrolytestests. In some instances, the method may be used in combination withurine creatinine test. In some instances, the method may be used incombination with urine protein test. In some instances, the method maybe used in combination with urine fractional excretion of sodium (FENA)test. In some instances, the method may be used in combination withglomerular filtration rate (GFR) test. In some instances, the method maybe used in combination with a renal biopsy. In some instances, themethod may be used in combination with one or more other gene expressiontests. In some instances, the method may be used when the result of theserum creatinine test indicates kidney dysfunction and/or transplantrejection. In some instances, the method may be used when the result ofthe glomerular filtration rate (GFR) test indicates kidney dysfunctionand/or transplant rejection. In some instances, the method may be usedwhen the result of the renal biopsy indicates kidney dysfunction and/ortransplant rejection. In some instances, the method may be used when theresult of one or more other gene expression tests indicates kidneydysfunction and/or transplant rejection.

Computer Program

The methods, kits, and systems disclosed herein may include at least onecomputer program, or use of the same. A computer program may include asequence of instructions, executable in the digital processing device'sCPU, written to perform a specified task. Computer readable instructionsmay be implemented as program modules, such as functions, objects,Application Programming Interfaces (APIs), data structures, and thelike, that perform particular tasks or implement particular abstractdata types. In light of the disclosure provided herein, those of skillin the art will recognize that a computer program may be written invarious versions of various languages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. The computer programwill normally provide a sequence of instructions from one location or aplurality of locations. In various embodiments, a computer programincludes, in part or in whole, one or more web applications, one or moremobile applications, one or more standalone applications, one or moreweb browser plug-ins, extensions, add-ins, or add-ons, or combinationsthereof.

Further disclosed herein are systems for classifying one or more samplesand uses thereof. The system may comprise (a) a digital processingdevice comprising an operating system configured to perform executableinstructions and a memory device; (b) a computer program includinginstructions executable by the digital processing device to classify asample from a subject comprising: (i) a first software module configuredto receive a gene expression profile of one or more genes from thesample from the subject; (ii) a second software module configured toanalyze the gene expression profile from the subject; and (iii) a thirdsoftware module configured to classify the sample from the subject basedon a classification system comprising three or more classes. At leastone of the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. At leasttwo of the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. All threeof the classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. Analyzingthe gene expression profile from the subject may comprise applying analgorithm. Analyzing the gene expression profile may comprisenormalizing the gene expression profile from the subject. In someinstances, normalizing the gene expression profile does not comprisequantile normalization.

FIG. 3 shows a computer system (also “system” herein) 401 programmed orotherwise configured for implementing the methods of the disclosure,such as producing a selector set and/or for data analysis. The system401 includes a central processing unit (CPU, also “processor” and“computer processor” herein) 405, which can be a single core or multicore processor, or a plurality of processors for parallel processing.The system 401 also includes memory 410 (e.g., random-access memory,read-only memory, flash memory), electronic storage unit 415 (e.g., harddisk), communications interface 420 (e.g., network adapter) forcommunicating with one or more other systems, and peripheral devices425, such as cache, other memory, data storage and/or electronic displayadapters. The memory 410, storage unit 415, interface 420 and peripheraldevices 425 are in communication with the CPU 405 through acommunications bus (solid lines), such as a motherboard. The storageunit 415 can be a data storage unit (or data repository) for storingdata. The system 401 is operatively coupled to a computer network(“network”) 430 with the aid of the communications interface 420. Thenetwork 430 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 430 in some instances is a telecommunication and/or datanetwork. The network 430 can include one or more computer servers, whichcan enable distributed computing, such as cloud computing. The network430 in some instances, with the aid of the system 401, can implement apeer-to-peer network, which may enable devices coupled to the system 401to behave as a client or a server.

The system 401 is in communication with a processing system 435. Theprocessing system 435 can be configured to implement the methodsdisclosed herein. In some examples, the processing system 435 is anucleic acid sequencing system, such as, for example, a next generationsequencing system (e.g., Illumina sequencer, Ion Torrent sequencer,Pacific Biosciences sequencer). The processing system 435 can be incommunication with the system 401 through the network 430, or by direct(e.g., wired, wireless) connection. The processing system 435 can beconfigured for analysis, such as nucleic acid sequence analysis.

Methods as described herein can be implemented by way of machine (orcomputer processor) executable code (or software) stored on anelectronic storage location of the system 401, such as, for example, onthe memory 410 or electronic storage unit 415. During use, the code canbe executed by the processor 405. In some examples, the code can beretrieved from the storage unit 415 and stored on the memory 410 forready access by the processor 405. In some situations, the electronicstorage unit 415 can be precluded, and machine-executable instructionsare stored on memory 410.

Digital Processing Device

The methods, kits, and systems disclosed herein may include a digitalprocessing device, or use of the same. In further embodiments, thedigital processing device includes one or more hardware centralprocessing units (CPU) that carry out the device's functions. In stillfurther embodiments, the digital processing device further comprises anoperating system configured to perform executable instructions. In someembodiments, the digital processing device is optionally connected acomputer network. In further embodiments, the digital processing deviceis optionally connected to the Internet such that it accesses the WorldWide Web. In still further embodiments, the digital processing device isoptionally connected to a cloud computing infrastructure. In otherembodiments, the digital processing device is optionally connected to anintranet. In other embodiments, the digital processing device isoptionally connected to a data storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers,handheld computers, Internet appliances, mobile smartphones, tabletcomputers, personal digital assistants, video game consoles, andvehicles. Those of skill in the art will recognize that many smartphonesare suitable for use in the system described herein. Those of skill inthe art will also recognize that select televisions, video players, anddigital music players with optional computer network connectivity aresuitable for use in the system described herein. Suitable tabletcomputers include those with booklet, slate, and convertibleconfigurations, known to those of skill in the art.

The digital processing device will normally include an operating systemconfigured to perform executable instructions. The operating system is,for example, software, including programs and data, which manages thedevice's hardware and provides services for execution of applications.Those of skill in the art will recognize that suitable server operatingsystems include, by way of non-limiting examples, FreeBSD, OpenBSD,NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, WindowsServer®, and Novell® NetWare®. Those of skill in the art will recognizethat suitable personal computer operating systems include, by way ofnon-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, andUNIX-like operating systems such as GNU/Linux®. In some embodiments, theoperating system is provided by cloud computing. Those of skill in theart will also recognize that suitable mobile smart phone operatingsystems include, by way of non-limiting examples, Nokia® Symbian® OS,Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®,Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, andPalm® WebOS®.

The device generally includes a storage and/or memory device. Thestorage and/or memory device is one or more physical apparatuses used tostore data or programs on a temporary or permanent basis. In someembodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the digitalprocessing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In otherembodiments, the device is a storage device including, by way ofnon-limiting examples, CD-ROMs, DVDs, flash memory devices, magneticdisk drives, magnetic tapes drives, optical disk drives, and cloudcomputing based storage. In further embodiments, the storage and/ormemory device is a combination of devices such as those disclosedherein.

A display to send visual information to a user will normally beinitialized. Examples of displays include a cathode ray tube (CRT, aliquid crystal display (LCD), a thin film transistor liquid crystaldisplay (TFT-LCD, an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display may be a plasma display, a video projector or a combinationof devices such as those disclosed herein.

The digital processing device would normally include an input device toreceive information from a user. The input device may be, for example, akeyboard, a pointing device including, by way of non-limiting examples,a mouse, trackball, track pad, joystick, game controller, or stylus; atouch screen, or a multi-touch screen, a microphone to capture voice orother sound input, a video camera to capture motion or visual input or acombination of devices such as those disclosed herein.

Non-Transitory Computer Readable Storage Medium

The methods, kits, and systems disclosed herein may include one or morenon-transitory computer readable storage media encoded with a programincluding instructions executable by the operating system to perform andanalyze the test described herein; preferably connected to a networkeddigital processing device. The computer readable storage medium is atangible component of a digital that is optionally removable from thedigital processing device. The computer readable storage mediumincludes, by way of non-limiting examples, CD-ROMs, DVDs, flash memorydevices, solid state memory, magnetic disk drives, magnetic tape drives,optical disk drives, cloud computing systems and services, and the like.In some instances, the program and instructions are permanently,substantially permanently, semi-permanently, or non-transitorily encodedon the media.

A non-transitory computer-readable storage media may be encoded with acomputer program including instructions executable by a processor tocreate or use a classification system. The storage media may comprise(a) a database, in a computer memory, of one or more clinical featuresof two or more control samples, wherein (i) the two or more controlsamples may be from two or more subjects; and (ii) the two or morecontrol samples may be differentially classified based on aclassification system comprising three or more classes; (b) a firstsoftware module configured to compare the one or more clinical featuresof the two or more control samples; and (c) a second software moduleconfigured to produce a classifier set based on the comparison of theone or more clinical features.

At least two of the classes may be selected from transplant rejection,transplant dysfunction with no rejection and normal transplant function.All three classes may be selected from transplant rejection, transplantdysfunction with no rejection and normal transplant function. Thestorage media may further comprise one or more additional softwaremodules configured to classify a sample from a subject. Classifying thesample from the subject may comprise a classification system comprisingthree or more classes. At least two of the classes may be selected fromtransplant rejection, transplant dysfunction with no rejection andnormal transplant function. All three classes may be selected fromtransplant rejection, transplant dysfunction with no rejection andnormal transplant function.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and XML database systems. In further embodiments, suitablerelational database systems include, by way of non-limiting examples,Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the artwill also recognize that a web application, in various embodiments, iswritten in one or more versions of one or more languages. A webapplication may be written in one or more markup languages, presentationdefinition languages, client-side scripting languages, server-sidecoding languages, database query languages, or combinations thereof. Insome embodiments, a web application is written to some extent in amarkup language such as Hypertext Markup Language (HTML), ExtensibleHypertext Markup Language (XHTML), or eXtensible Markup Language (XML).In some embodiments, a web application is written to some extent in apresentation definition language such as Cascading Style Sheets (CSS).In some embodiments, a web application is written to some extent in aclient-side scripting language such as Asynchronous Javascript and XML(AJAX), Flash® Actionscript, Javascript, or Silverlight®. In someembodiments, a web application is written to some extent in aserver-side coding language such as Active Server Pages (ASP),ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor(PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In someembodiments, a web application is written to some extent in a databasequery language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBM® Lotus Domino®. In some embodiments, a web applicationincludes a media player element. In various further embodiments, a mediaplayer element utilizes one or more of many suitable multimediatechnologies including, by way of non-limiting examples, Adobe® Flash®,HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile digital processing device. In some embodiments, themobile application is provided to a mobile digital processing device atthe time it is manufactured. In other embodiments, the mobileapplication is provided to a mobile digital processing device via thecomputer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C#, Objective-C, Java™, Javascript,Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML withor without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Android™ Market, BlackBerry®App World, App Store for Palm devices, App Catalog for webOS, Windows®Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, andNintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable complied applications.

Web Browser Plug-in

In some embodiments, the computer program includes a web browserplug-in. In computing, a plug-in is one or more software components thatadd specific functionality to a larger software application. Makers ofsoftware applications support plug-ins to enable third-party developersto create abilities which extend an application, to support easilyadding new features, and to reduce the size of an application. Whensupported, plug-ins enable customizing the functionality of a softwareapplication. For example, plug-ins are commonly used in web browsers toplay video, generate interactivity, scan for viruses, and displayparticular file types. Those of skill in the art will be familiar withseveral web browser plug-ins including, Adobe® Flash® Player, Microsoft®Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbarcomprises one or more web browser extensions, add-ins, or add-ons. Insome embodiments, the toolbar comprises one or more explorer bars, toolbands, or desk bands.

In view of the disclosure provided herein, those of skill in the artwill recognize that several plug-in frameworks are available that enabledevelopment of plug-ins in various programming languages, including, byway of non-limiting examples, C++, Delphi, Java™ PHP, Python™, and VB.NET, or combinations thereof.

Web browsers (also called Internet browsers) are software applications,designed for use with network-connected digital processing devices, forretrieving, presenting, and traversing information resources on theWorld Wide Web. Suitable web browsers include, by way of non-limitingexamples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google®Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. Insome embodiments, the web browser is a mobile web browser. Mobile webbrowsers (also called mircrobrowsers, mini-browsers, and wirelessbrowsers) are designed for use on mobile digital processing devicesincluding, by way of non-limiting examples, handheld computers, tabletcomputers, netbook computers, subnotebook computers, smartphones, musicplayers, personal digital assistants (PDAs), and handheld video gamesystems. Suitable mobile web browsers include, by way of non-limitingexamples, Google® Android® browser, RIM BlackBerry® Browser, Apple®Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® formobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web,Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

Software Modules

The methods, kits, and systems disclosed herein may include software,server, and/or database modules, or use of the same. In view of thedisclosure provided herein, software modules are created by techniquesknown to those of skill in the art using machines, software, andlanguages known to the art. The software modules disclosed herein areimplemented in a multitude of ways. In various embodiments, a softwaremodule comprises a file, a section of code, a programming object, aprogramming structure, or combinations thereof. In further variousembodiments, a software module comprises a plurality of files, aplurality of sections of code, a plurality of programming objects, aplurality of programming structures, or combinations thereof. In variousembodiments, the one or more software modules comprise, by way ofnon-limiting examples, a web application, a mobile application, and astandalone application. In some embodiments, software modules are in onecomputer program or application. In other embodiments, software modulesare in more than one computer program or application. In someembodiments, software modules are hosted on one machine. In otherembodiments, software modules are hosted on more than one machine. Infurther embodiments, software modules are hosted on cloud computingplatforms. In some embodiments, software modules are hosted on one ormore machines in one location. In other embodiments, software modulesare hosted on one or more machines in more than one location.

Databases

The methods, kits, and systems disclosed herein may comprise one or moredatabases, or use of the same. In view of the disclosure providedherein, those of skill in the art will recognize that many databases aresuitable for storage and retrieval of information pertaining to geneexpression profiles, sequencing data, classifiers, classificationsystems, therapeutic regimens, or a combination thereof. In variousembodiments, suitable databases include, by way of non-limitingexamples, relational databases, non-relational databases, objectoriented databases, object databases, entity-relationship modeldatabases, associative databases, and XML databases. In someembodiments, a database is internet-based. In further embodiments, adatabase is web-based. In still further embodiments, a database is cloudcomputing-based. In other embodiments, a database is based on one ormore local computer storage devices.

Data Transmission

The methods, kits, and systems disclosed herein may be used to transmitone or more reports. The one or more reports may comprise informationpertaining to the classification and/or identification of one or moresamples from one or more subjects. The one or more reports may compriseinformation pertaining to a status or outcome of a transplant in asubject. The one or more reports may comprise information pertaining totherapeutic regimens for use in treating transplant rejection in asubject in need thereof. The one or more reports may compriseinformation pertaining to therapeutic regimens for use in treatingtransplant dysfunction in a subject in need thereof. The one or morereports may comprise information pertaining to therapeutic regimens foruse in suppressing an immune response in a subject in need thereof.

The one or more reports may be transmitted to a subject or a medicalrepresentative of the subject. The medical representative of the subjectmay be a physician, physician's assistant, nurse, or other medicalpersonnel. The medical representative of the subject may be a familymember of the subject. A family member of the subject may be a parent,guardian, child, sibling, aunt, uncle, cousin, or spouse. The medicalrepresentative of the subject may be a legal representative of thesubject.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. Although any methods and materials similaror equivalent to those described herein can be used in the practice ortesting of the present invention, the preferred methods and materialsare described.

All publications, GenBank sequences, ATCC deposits, patents and patentapplications cited herein are hereby expressly incorporated by referencein their entirety and for all purposes as if each is individually sodenoted.

TABLE 1 Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples) Validation CohortPredictive Postive Negative Accuracy Sensitivity Specificity PredictivePredictive Algorithm Predictors Comparison AUC (%) (%) (%) Value (%)Value (%) Nearest Centroid 199 AR vs. TX 0.957 95 96 96 94 97 NearestCentroid 199 ADNR vs. TX 0.977 97 94 100 100 97 Nearest Centroid 199 CANvs. TX 0.992 99 98 100 100 99

TABLE 2 Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (Brazilian Samples) ValidationCohort Predictive Postive Negative Accuracy Sensitivity SpecificityPredictive Predictive Algorithm Predictors Comparison AUC (%) (%) (%)Value (%) Value (%) Nearest Centroid 199 AR vs. TX 0.976 98 100 95 95100 Nearest Centroid 199 ADNR vs. TX 1.000 100 100 100 100 100 NearestCentroid 199 CAN vs. TX 1.000 100 100 100 100 100

TABLE 3 Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (TGCG Samples) Validation CohortPredictive Postive Negative Accuracy Sensitivity Specificity PredictivePredictive Algorithm Predictors Comparison AUC (%) (%) (%) Value (%)Value (%) Nearest Centroid 197 AR vs. TX 0.979 98 96 100 100 96 NearestCentroid 197 ADNR vs. TX 0.987 99 97 100 100 98 Nearest Centroid 197 ARvs. ADNR 0.968 97 100 93 95 100

TABLE 4 Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (Brazilian Samples) Validation CohortPredictive Postive Negative Accuracy Sensitivity Specificity PredictivePredictive Algorithm Predictors Comparison AUC (%) (%) (%) Value (%)Value (%) Nearest Centroid 197 AR vs. TX 0.976 98 100 95 95 100 NearestCentroid 197 ADNR vs. TX 1.000 100 100 100 100 100 Nearest Centroid 197AR vs. ADNR 0.962 97 100 91 95 100

TABLE 5 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN vs. TX (TGCG Samples) Validation Cohort PredictivePostive Negative Accuracy Sensitivity Specificity Predictive PredictiveAlgorithm Predictors Comparison AUC (%) (%) (%) Value (%) Value (%)Nearest Centroid 200 AR vs. TX 0.965 97 96 97 96 97

TABLE 6 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN vs. TX (Brazilian Samples) Validation Cohort PredictivePostive Negative Accuracy Sensitivity Specificity Predictive PredictiveAlgorithm Predictors Comparison AUC (%) (%) (%) Value (%) Value (%)Nearest Centroid 200 AR vs. TX 0.954 95 95 96 95 96

TABLE 7 Biopsy Expression Profiling of Kidney Transplants: 4-WayClassifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples) p-value Entrez(Final ADNR- AR- CAN- TX- # Probeset ID Gene Gene Symbol Gene TitlePhenotype) Mean Mean Mean Mean 1 204446_PM_s_at 240 ALOX5 arachidonate5-lipoxygenase 2.82E−34 91.9 323.9 216.7 54.7 2 202207_PM_at 10123 ARL4CADP-ribosylation factor-like 4C 1.31E−32 106.9 258.6 190.4 57.2 3204698_PM_at 3669 ISG20 interferon stimulated exonuclease 1.50E−31 41.5165.1 96.1 27.6 gene 20 kDa 4 225701_PM_at 80709 AKNA AT-hooktranscription factor 1.75E−31 37.7 102.8 73.2 29.0 5 207651_PM_at 29909GPR171 G protein-coupled receptor 171 6.30E−31 25.8 89.9 57.0 20.9 6204205_PM_at 60489 APOBEC3G apolipoprotein B mRNA editing 1.27E−30 95.4289.4 192.0 78.7 enzyme, catalytic polypeptide-like 3G 7 208948_PM_s_at6780 STAU1 staufen, RNA binding protein, homolog 1.37E−30 1807.9 1531.81766.0 2467.4 1 (Drosophila) 8 217733_PM_s_at 9168 TMSB10 thymosin beta10 2.38E−30 4414.7 6331.3 5555.2 3529.0 9 205831_PM_at 914 CD2 CD2molecule 2.73E−30 40.4 162.5 100.9 33.9 10 209083_PM_at 11151 CORO1Acoronin, actin binding protein, 1A 5.57E−30 46.9 163.8 107.1 34.3 11210915_PM_x_at 28638 TRBC2 T cell receptor beta constant 2 5.60E−30 39.7230.7 129.7 37.5 12 211368_PM_s_at 834 CASP1 caspase 1,apoptosis-related cysteine 6.21E−30 102.6 274.3 191.4 81.8 peptidase(interleukin 1, beta, convertase) 13 201042_PM_at 7052 TGM2transglutaminase 2 (C polypeptide, 6.28E−30 131.8 236.5 172.6 80.1protein-glutamine-gamma- glutamyltransferase) 14 227353_PM_at 147138TMC8 transmembrane channel-like 8 7.76E−30 19.8 64.2 42.7 16.6 151555852_P_M_at 100507463 LOC10050746 hypothetical LOC100507463 8.29E−3078.9 202.6 154.2 70.9 3 16 226878_PM_at 3111 HLA-DOA majorhistocompatibility complex, class 1.63E−29 102.0 288.9 201.4 94.3 II, DOalpha 17 238327_PM_at 440836 ODF3B outer dense fiber of sperm tails 3B1.74E−29 32.8 81.4 58.5 26.1 18 229437_PM_at 114614 MIR155HG MIR155 hostgene (non-protein coding) 1.78E−29 15.4 50.4 28.5 12.9 19 33304_PM_at3669 ISG20 interferon stimulated exonuclease 2.40E−29 33.2 101.3 63.422.1 gene 20 kDa 20 226621_PM_at 9180 OSMR oncostatin M receptor2.42E−29 545.6 804.5 682.9 312.1 21 1553906_PM_s_at 221472 FGD2 FYVE,RhoGEF and PH domain 2.43E−29 104.6 321.0 219.3 71.9 containing 2 221405_PM_i_at 6352 CCL5 chemokine (C-C motif) ligand 5 2.54E−29 68.0295.7 195.6 54.6 23 226219_PM_at 257106 ARHGAP30 Rho GTPase activatingprotein 30 2.92E−29 46.4 127.9 91.8 37.5 24 204891_PM_s_at 3932 LCKlymphocyte-specific protein tyrosine 3.79E−29 19.3 74.2 43.4 17.8 kinase25 210538_PM_s_at 330 BIRC3 baculoviral IAP repeat-containing 3 5.06E−29106.7 276.7 199.1 84.6 26 202644_PM_s_at 7128 TNFAIP3 tumor necrosisfactor, alpha-induced 5.47E−29 169.8 380.4 278.2 136.6 protein 3 27227346_PM_at 10320 IKZF1 IKAROS family zinc finger 1 (Ikaros) 7.07E−2924.9 79.7 53.3 19.8 28 202957_PM_at 3059 HCLS1 hematopoieticcell-specific Lyn 8.26E−29 119.2 299.5 229.9 82.2 substrate 1 29202307_PM_s_at 6890 TAP1 transporter 1, ATP-binding cassette, 1.01E−28172.4 420.6 280.0 141.0 sub-family B (MDR/TAP) 30 202748_PM_at 2634 GBP2guanylate binding protein 2, 1.10E−28 196.7 473.0 306.7 141.0interferon-inducible 31 211796_PM_s_at 28638 /// TRBC1 /// T cellreceptor beta constant 1 /// T 1.31E−28 69.2 431.5 250.2 63.6 28639TRBC2 cell receptor beta constant 2 32 213160_PM_at 1794 DOCK2 dedicatorof cytokinesis 2 1.36E−28 33.7 92.6 66.0 27.8 33 211656_PM_x_at100133583 HLA-DQB1 /// major histocompatibility complex, class 1.63E−28211.3 630.2 459.8 208.2 /// 3119 LOC10013358 II, DQ beta 1 /// HLA classII 3 histocompatibili 34 223322_PM_at 83593 RASSF5 Ras association(RaIGDS/AF-6) domain 1.68E−28 41.5 114.4 79.3 39.2 family member 5 35205488_PM_at 3001 GZMA granzyme A (granzyme 1, cytotoxic 1- 1.72E−2837.3 164.8 102.3 33.4 lymphocyte-associated serine esterase 3) 36213603_PM_s_at 5880 RAC2 ras-related C3 botulinum toxin 1.87E−28 113.9366.5 250.3 86.5 substrate 2 (rho family, small GTP binding proteinRac2) 37 229390_PM_at 441168 FAM26F family with sequence similarity 26,1.94E−28 103.8 520.0 272.4 75.9 member F 38 206804_PM_at 917 CD3G CD3gmolecule, gamma (CD3-TCR 1.99E−28 19.7 60.6 36.4 17.3 complex) 39209795_PM_at 969 CD69 CD69 molecule 2.06E−28 17.6 57.6 40.6 15.2 40219574_PM_at 55016 1-Mar membrane-associated ring finger 2.07E−28 51.5126.0 87.5 36.2 (C3HC4) 1 41 207320_PM_x_at 6780 STAU1 staufen, RNAbinding protein, homolog 2.21E−28 1425.1 1194.6 1383.2 1945.3 1(Drosophila) 42 218983_PM_at 51279 C1RL complement component 1, r2.97E−28 167.1 244.5 206.9 99.4 subcomponent-like 43 206011_PM_at 834CASP1 caspase 1, apoptosis-related cysteine 3.23E−28 74.5 198.0 146.260.7 peptidase (interleukin 1, beta, convertase) 44 213539_PM_at 915CD3D CD3d molecule, delta (CD3-TCR 5.42E−28 70.8 335.1 168.1 60.0complex) 45 213193_PM_x_at 28639 TRBC1 T cell receptor beta constant 15.69E−28 95.3 490.9 286.5 92.1 46 232543_PM_x_at 64333 ARHGAP9 RhoGTPase activating protein 9 6.79E−28 31.7 99.1 62.3 25.8 47 200986_PM_at710 SERPING1 serpin peptidase inhibitor, clade G (C1 7.42E−28 442.7731.5 590.2 305.3 inhibitor), member 1 48 213037_PM_x_at 6780 STAU1staufen, RNA binding protein, homolog 9.35E−28 1699.0 1466.8 1670.12264.9 1 (Drosophila) 49 204670_PM_x_at 3123 /// HLA-DRB1 /// majorhistocompatibility complex, class 1.03E−27 2461.9 4344.6 3694.9 2262.03126 HLA-DRB4 II, DR beta 1 /// major histocompatibility comp 50217028_PM_at 7852 CXCR4 chemokine (C-X-C motif) receptor 4 1.52E−27100.8 304.4 208.4 75.3 51 203761_PM_at 6503 SLA Src-like-adaptor1.61E−27 69.6 179.2 138.4 51.4 52 201137_PM_s_at 3115 HLA-DPB1 majorhistocompatibility complex, class 1.95E−27 1579.1 3863.7 3151.7 1475.9II, DP beta 1 53 205269_PM_at 3937 LCP2 lymphocyte cytosolic protein 2(SH2 2.16E−27 30.2 92.2 58.9 22.9 domain containing leukocyte protein of76 kDa) 54 205821_PM_at 22914 KLRK1 killer cell lectin-like receptorsubfamily 2.56E−27 30.3 111.5 72.5 31.3 K, member 1 55 204655_PM_at 6352CCL5 chemokine (C-C motif) ligand 5 3.28E−27 77.5 339.4 223.4 66.8 56226474_PM_at 84166 NLRC5 NLR family, CARD domain containing 5 3.54E−2764.4 173.5 129.8 55.1 57 212503_PM_s_at 22982 DIP2C DIP2disco-interacting protein 2 3.69E−27 559.7 389.2 502.0 755.0 homolog C(Drosophila) 58 213857_PM_s_at 961 CD47 CD47 molecule 4.33E−27 589.6858.0 703.2 481.2 59 206118_PM_at 6775 STAT4 signal transducer andactivator of 4.58E−27 21.0 49.5 37.7 18.1 transcription 4 60227344_PM_at 10320 IKZF1 IKAROS family zinc finger 1 (Ikaros) 5.87E−2717.8 40.0 28.5 14.9 61 230550_PM_at 64231 MS4A6A membrane-spanning4-domains, 5.98E−27 44.8 124.3 88.0 30.9 subfamily A, member 6A 62235529_PM_x_at 25939 SAMHD1 SAM domain and HD domain 1 6.56E−27 189.3379.9 289.1 128.0 63 205758_PM_at 925 CD8A CD8a molecule 7.28E−27 24.2105.8 60.3 22.2 64 211366_PM_x_at 834 CASP1 caspase 1, apoptosis-relatedcysteine 7.37E−27 115.3 261.0 186.0 87.1 peptidase (interleukin 1, beta,convertase) 65 209606_PM_at 9595 CYTIP cytohesin 1 interacting protein7.48E−27 41.4 114.3 79.0 32.9 66 201721_PM_s_at 7805 LAPTM5 lysosomalprotein transmembrane 5 8.04E−27 396.5 934.6 661.3 249.4 67 204774_PM_at2123 EVI2A ecotropic viral integration site 2A 8.14E−27 63.6 168.5 114.744.9 68 215005_PM_at 54550 NECAB2 N-terminal EF-hand calcium binding8.32E−27 36.7 23.4 30.9 65.7 protein 2 69 229937_PM_x_at 10859 LILRB1Leukocyte immunoglobulin-like 8.33E−27 23.5 79.9 50.0 18.5 receptor,subfamily B (with TM and ITIM domains), member 70 209515_PM_s_at 5873RAB27A RAB27A, member RAS oncogene family 8.93E−27 127.3 192.2 160.585.2 71 242916_PM_at 11064 CEP110 centrosomal protein 110 kDa 8.98E−2730.8 68.1 51.2 26.2 72 205270_PM_s_at 3937 LCP2 lymphocyte cytosolicprotein 2 (SH2 9.04E−27 56.8 162.6 104.4 44.6 domain containingleukocyte protein of 76 kDa) 73 214022_PM_s_at 8519 IFITM1 interferoninduced transmembrane 9.31E−27 799.1 1514.7 1236.6 683.3 protein 1(9-27) 74 1552703_PM_s_at 114769 /// CARD16 /// caspase recruitmentdomain family, 1.01E−26 64.6 167.8 120.6 54.9 834 CASP1 member 16 ///caspase 1, apoptosis- related cysteine 75 202720_PM_at 26136 TES testisderived transcript (3 LIM 1.05E−26 285.4 379.0 357.9 204.2 domains) 76202659_PM_at 5699 PSMB10 proteasome (prosome, macropain) 1.10E−26 180.7355.6 250.3 151.5 subunit, beta type, 10 77 236295_PM_s_at 197358 NLRC3NLR family, CARD domain containing 3 1.19E−26 19.0 52.5 37.0 18.6 78229041_PM_s_at — — — 1.31E−26 36.5 132.1 84.7 32.4 79 205798_PM_at 3575IL7R interleukin 7 receptor 1.32E−26 44.1 136.8 106.3 33.4 80209970_PM_x_at 834 CASP1 caspase 1, apoptosis-related cysteine 1.36E−26116.0 266.6 181.6 88.7 peptidase (interleukin 1, beta, convertase) 81204336_PM_s_at 10287 RGS19 regulator of G-protein signaling 19 1.54E−2695.2 187.7 135.7 67.0 82 204912_PM_at 3587 IL10RA interleukin 10receptor, alpha 1.61E−26 57.0 178.7 117.2 46.1 83 227184_PM_at 5724PTAFR platelet-activating factor receptor 1.70E−26 89.8 191.4 134.4 62.784 209969_PM_s_at 6772 STAT1 signal transducer and activator of 1.82E−26395.8 1114.5 664.6 320.8 transcription 1, 91 kDa 85 232617_PM_at 1520CTSS cathepsin S 1.88E−26 209.6 537.9 392.2 154.8 86 224451_PM_x_at64333 ARHGAP9 Rho GTPase activating protein 9 1.94E−26 34.2 103.4 71.829.4 87 209670_PM_at 28755 TRAC T cell receptor alpha constant 2.06E−2637.9 149.9 96.2 38.5 88 1559584_PM_a_at 283897 C16orf54 chromosome 16open reading frame 2.22E−26 31.3 95.8 71.5 26.1 54 89 208306_PM_x_at3123 HLA-DRB1 Major histocompatibility complex, class 2.29E−26 2417.54278.5 3695.8 2255.0 II, DR beta 1 90 229383_PM_at 55016 1-Marmembrane-associated ring finger 2.36E−26 33.8 88.0 52.1 22.9 (C3HC4) 191 235735_PM_at — — — 2.46E−26 13.0 34.9 24.5 11.2 92 203416_PM_at 963CD53 CD53 molecule 2.56E−26 215.9 603.0 422.7 157.8 93 212504_PM_at22982 DIP2C DIP2 disco-interacting protein 2 3.21E−26 334.5 227.2 289.4452.5 homolog C (Drosophila) 94 204279_PM_at 5698 PSMB9 proteasome(prosome, macropain) 3.45E−26 241.6 637.4 419.4 211.3 subunit, betatype, 9 (large multifunctional peptidase 95 235964_PM_x_at 25939 SAMHD1SAM domain and HD domain 1 3.60E−26 172.9 345.9 270.1 117.5 96213566_PM_at 6039 RNASE6 ribonuclease, RNase A family, k6 3.84E−26 180.9482.0 341.1 134.3 97 221698_PM_s_at 64581 CLEC7A C-type lectin domainfamily 7, member 4.00E−26 61.5 164.6 112.8 49.7 A 98 227125_PM_at 3455IFNAR2 interferon (alpha, beta and omega) 4.03E−26 70.0 126.2 96.7 55.8receptor 2 99 226525_PM_at 9262 STK17B serine/threonine kinase 17b4.14E−26 146.8 338.7 259.1 107.6 100 221666_PM_s_at 29108 PYCARD PYD andCARD domain containing 4.95E−26 60.7 132.8 95.5 44.7 101 209774_PM_x_at2920 CXCL2 chemokine (C-X-C motif) ligand 2 5.73E−26 24.9 52.9 38.2 15.5102 206082_PM_at 10866 HCP5 HLA complex P5 5.98E−26 76.2 185.1 129.066.6 103 229391_PM_s_at 441168 FAM26F family with sequence similarity26, 6.03E−26 98.6 379.7 212.1 73.7 member F 104 229295_PM_at 150166 ///IL17RA /// interleukin 17 receptor A /// 6.13E−26 76.4 131.8 98.4 50.023765 LOC150166 hypothetical protein LOC150166 105 202901_PM_x_at 1520CTSS cathepsin S 6.32E−26 67.8 180.5 130.9 45.1 106 226991_PM_at 4773NFATC2 nuclear factor of activated T-cells, 6.49E−26 37.9 87.0 66.7 30.3cytoplasmic, calcineurin-dependent 2 107 223280_PM_x_at 64231 MS4A6Amembrane-spanning 4-domains, 6.72E−26 269.0 711.8 451.2 199.5 subfamilyA, member 6A 108 201601_PM_x_at 8519 IFITM1 interferon inducedtransmembrane 7.27E−26 1471.3 2543.5 2202.5 1251.1 protein 1 (9-27) 1091552701_PM_a_at 114769 CARD16 caspase recruitment domain family,7.33E−26 143.8 413.6 273.3 119.8 member 16 110 229625_PM_at 115362 GBPSguanylate binding protein 5 7.80E−26 29.3 133.3 68.6 24.0 11138149_PM_at 9938 ARHGAP25 Rho GTPase activating protein 25 9.83E−26 51.3108.9 83.2 43.2 112 203932_PM_at 3109 HLA-DMB major histocompatibilitycomplex, class 1.03E−25 422.4 853.9 633.5 376.0 II, DM beta 113228964_PM_at 639 PRDM1 PR domain containing 1, with ZNF 1.15E−25 21.252.1 41.5 17.0 domain 114 225799_PM_at 112597 /// LOC541471 hypotheticalLOC541471 /// non- 1.23E−25 230.5 444.3 339.2 172.0 541471 /// proteincoding RNA 152 NCRNA00152 115 204118_PM_at 962 CD48 CD48 molecule1.34E−25 82.9 341.5 212.4 65.2 116 211742_PM_s_at 2124 EVI2B ecotropicviral integration site 2B 1.36E−25 73.9 236.5 166.2 53.2 117213416_PM_at 3676 ITGA4 integrin, alpha 4 (antigen CD49D, alpha 1.47E−2526.3 78.1 50.8 22.8 4 subunit of VLA-4 receptor) 118 211991_PM_s_at 3113HLA-DPA1 major histocompatibility complex, class 1.50E−25 1455.0 3605.42837.2 1462.9 II, DP alpha 1 119 232024_PM_at 26157 GIMAP2 GTPase, IMAPfamily member 2 1.57E−25 90.2 197.7 146.7 72.5 120 205159_PM_at 1439CSF2RB colony stimulating factor 2 receptor, 1.73E−25 33.7 107.5 70.426.3 beta, low-affinity (granulocyte- macrophage) 121 228471_PM_at 91526ANKRD44 ankyrin repeat domain 44 1.79E−25 106.1 230.3 184.6 86.5 122203332_PM_s_at 3635 INPP5D inositol polyphosphate-5-phosphatase,1.88E−25 27.9 60.5 42.6 24.0 145 kDa 123 223502_PM_s_at 10673 INFSF13Btumor necrosis factor (ligand) 2.02E−25 73.0 244.3 145.5 60.0superfamily, member 13b 124 229723_PM_at 117289 TAGAP T-cell activationRhoGTPase activating 2.07E−25 29.2 82.9 55.9 26.2 protein 125206978_PM_at 729230 CCR2 chemokine (C-C motif) receptor 2 2.17E−25 32.1100.7 68.6 27.3 126 1555832_PM_s_at 1316 KLF6 Kruppel-like factor 62.31E−25 899.4 1076.8 1003.3 575.1 127 211990_PM_at 3113 HLA-DPA1 majorhistocompatibility complex, class 2.53E−25 2990.7 5949.1 5139.9 3176.3II, DP alpha 1 128 202018_PM_s_at 4057 LTF lactotransferrin 2.90E−25392.3 1332.4 624.5 117.7 129 210644_PM_s_at 3903 LAIR1leukocyte-associated immunoglobulin- 2.90E−25 29.7 74.6 45.3 21.2 likereceptor 1 130 222294_PM_s_at 5873 RAB27A RAB27A, member RAS oncogenefamily 3.13E−25 198.7 309.1 263.0 146.4 131 238668_PM_at — — — 3.29E−2518.2 49.2 33.6 14.5 132 213975_PM_s_at 4069 LYZ lysozyme 3.31E−25 458.41626.0 1089.7 338.1 133 204220_PM_at 9535 GMFG glia maturation factor,gamma 3.46E−25 147.0 339.3 241.4 128.9 134 243366_PM_s_at — — — 3.46E−2524.7 72.1 52.5 22.0 135 221932_PM_s_at 51218 GLRX5 glutaredoxin 53.64E−25 1351.5 1145.8 1218.1 1599.3 136 225415_PM_at 151636 DTX3Ldeltex 3-like (Drosophila) 3.77E−25 230.2 376.4 290.8 166.9 137205466_PM_s_at 9957 HS3ST1 heparan sulfate (glucosamine) 3-O- 4.15E−2573.6 123.8 96.0 42.1 sulfotransferase 1 138 200904_PM_at 3133 HLA-Emajor histocompatibility complex, class 4.20E−25 1142.5 1795.2 1607.7994.7 I, E 139 228442_PM_at 4773 NFATC2 nuclear factor of activatedT-cells, 4.48E−25 39.0 84.8 62.8 32.0 cytoplasmic, calcineurin-dependent2 140 204923_PM_at 54440 SASH3 SAM and SH3 domain containing 3 4.49E−2525.4 68.2 47.6 21.7 141 223640_PM_at 10870 HCST hematopoietic cellsignal transducer 4.52E−25 91.0 234.0 158.3 72.7 142 211582_PM_x_at 7940LST1 leukocyte specific transcript 1 4.53E−25 57.5 183.8 121.2 49.4 143219014_PM_at 51316 PLAC8 placenta-specific 8 5.94E−25 38.8 164.1 88.630.7 144 210895_PM_s_at 942 CD86 CD86 molecule 6.21E−25 32.3 85.0 52.621.6 145 AFFX- 6772 STAT1 signal transducer and activator of 6.81E−25642.1 1295.1 907.8 539.6 HUMISGF3A/ transcription 1, 91 kDa M97935_3_at146 201315_PM_x_at 10581 IFITM2 interferon induced transmembrane6.87E−25 2690.9 3712.1 3303.7 2175.3 protein 2 (1-8D) 147 228532_PM_at128346 C1orf162 chromosome 1 open reading frame 7.07E−25 82.6 217.7140.2 60.0 162 148 202376_PM_at 12 SERPINA3 serpin peptidase inhibitor,clade A 7.13E−25 186.2 387.1 210.2 51.7 (alpha-1 antiproteinase,antitrypsin), member 3 149 212587_PM_s_at 5788 PTPRC protein tyrosinephosphatase, receptor 7.18E−25 114.8 398.6 265.7 90.3 type, C 150223218_PM_s_at 64332 NFKBIZ nuclear factor of kappa light 7.26E−25 222.6497.9 399.9 159.1 polypeptide gene enhancer in B-cells inhibitor, zeta151 224356_PM_x_at 64231 MS4A6A membrane-spanning 4-domains, 7.33E−25150.6 399.6 249.6 111.2 subfamily A, member 6A 152 206420_PM_at 10261IGSF6 immunoglobulin superfamily, member 7.58E−25 45.1 131.5 74.3 32.7 6153 225764_PM_at 2120 ETV6 ets variant 6 7.66E−25 92.6 133.0 112.8 77.0154 1555756_PM_a_at 64581 CLEC7A C-type lectin domain family 7, member7.74E−25 16.6 45.4 28.9 13.2 A 155 226218_PM_at 3575 IL7R interleukin 7receptor 8.14E−25 55.6 197.0 147.1 41.4 156 209198_PM_s_at 23208 SYT11synaptotagmin XI 8.28E−25 30.0 45.3 41.8 22.8 157 202803_PM_s_at 3689ITGB2 integrin, beta 2 (complement 9.57E−25 100.5 253.0 182.6 65.2component 3 receptor 3 and 4 subunit) 158 215049_PM_x_at 9332 CD163CD163 molecule 9.85E−25 232.8 481.3 344.5 112.9 159 202953_PM_at 713C1QB complement component 1, q 9.99E−25 215.8 638.4 401.1 142.5subcomponent, B chain 160 208091_PM_s_at 81552 VOPP1 vesicular,overexpressed in cancer, 1.02E−24 495.5 713.9 578.1 409.7 prosurvivalprotein 1 161 201288_PM_at 397 ARHGDIB Rho GDP dissociation inhibitor(GDI) 1.13E−24 354.9 686.8 542.2 308.1 beta 162 213733_PM_at 4542 MYO1Fmyosin IF 1.27E−24 26.8 52.7 39.4 20.9 163 212588_PM_at 5788 PTPRCprotein tyrosine phosphatase, receptor 1.41E−24 94.4 321.0 217.7 76.4type, C 164 242907_PM_at — — — 1.49E−24 59.3 165.1 99.7 39.8 165209619_PM_at 972 CD74 CD74 molecule, major 1.55E−24 989.0 1864.7 1502.3864.9 histocompatibility complex, class II invariant chain 166239237_PM_at — — — 1.75E−24 15.9 34.9 25.3 14.5 167 217022_PM_s_at100126583 IGHA1 /// immunoglobulin heavy constant alpha 1.80E−24 77.7592.5 494.6 49.4 /// 3493 /// IGHA2 /// 1 /// immunoglobulin heavyconstant 3494 LOC10012658 alpha 2 (A2m ma 3 168 201859_PM_at 5552 SRGNserglycin 1.82E−24 1237.9 2171.9 1747.0 981.8 169 243418_PM_at — — —1.88E−24 56.3 31.1 49.8 104.8 170 202531_PM_at 3659 IRF1 interferonregulatory factor 1 1.93E−24 92.9 226.0 154.5 77.0 171 208966_PM_x_at3428 IFI16 interferon, gamma-inducible protein 1.98E−24 406.7 760.4644.9 312.6 16 172 1555759_PM_a_at 6352 CCL5 chemokine (C-C motif)ligand 5 2.02E−24 81.4 350.8 233.2 68.3 173 202643_PM_s_at 7128 TNFAIP3tumor necrosis factor, alpha-induced 2.11E−24 43.7 92.8 68.1 34.8protein 3 174 223922_PM_x_at 64231 MS4A6A membrane-spanning 4-domains,2.22E−24 289.2 656.8 424.1 214.5 subfamily A, member 6A 175209374_PM_s_at 3507 IGHM immunoglobulin heavy constant mu 2.26E−24 61.8437.0 301.0 45.0 176 227677_PM_at 3718 JAK3 Janus kinase 3 2.29E−24 18.651.7 32.0 15.5 177 221840_PM_at 5791 PTPRE protein tyrosine phosphatase,receptor 2.38E−24 71.0 133.2 102.5 51.8 type, E 178 200887_PM_s_at 6772STAT1 signal transducer and activator of 2.47E−24 1141.7 2278.6 1602.9972.9 transcription 1, 91 kDa 179 221875_PM_x_at 3134 HLA-F majorhistocompatibility complex, class 2.72E−24 1365.3 2400.6 1971.8 1213.0I, F 180 206513_PM_at 9447 AIM2 absent in melanoma 2 2.87E−24 17.2 50.730.5 13.9 181 214574_PM_x_at 7940 LST1 leukocyte specific transcript 12.95E−24 74.1 222.8 142.0 61.7 182 231776_PM_at 8320 EOMES eomesodermin3.07E−24 24.0 63.9 43.5 22.4 183 205639_PM_at 313 AOAH acyloxyacylhydrolase (neutrophil) 4.03E−24 30.3 72.6 45.3 25.2 184 201762_PM_s_at5721 PSME2 proteasome (prosome, macropain) 4.45E−24 1251.6 1825.1 1423.41091.0 activator subunit 2 (PA28 beta) 185 217986_PM_s_at 11177 BAZ1Abromodomain adjacent to zinc finger 4.79E−24 87.5 145.2 116.4 62.5domain, 1A 186 235229_PM_at — — — 4.84E−24 50.9 210.6 135.0 41.9 187204924_PM_at 7097 TLR2 toll-like receptor 2 4.84E−24 96.8 162.0 116.666.6 188 202208_PM_s_at 10123 ARL4C ADP-ribosylation factor-like 4C4.89E−24 54.0 99.6 77.0 42.2 189 227072_PM_at 25914 RUN rotatin 5.01E−24101.1 74.5 83.6 132.9 190 202206_PM_at 10123 ARL4C ADP-ribosylationfactor-like 4C 5.08E−24 60.8 128.5 96.1 36.0 191 204563_PM_at 6402 SELLselectin L 5.11E−24 40.7 134.7 76.1 31.7 192 219386_PM_s_at 56833 SLAMF8SLAM family member 8 5.17E−24 28.2 92.1 52.2 19.4 193 218232_PM_at 712C1QA complement component 1, q 5.88E−24 128.8 287.1 197.0 85.8subcomponent, A chain 194 232311_PM_at 567 B2M Beta-2-microglobulin6.06E−24 42.3 118.6 83.7 35.2 195 219684_PM_at 64108 RTP4 receptor(chemosensory) transporter 6.09E−24 63.1 129.3 93.7 50.4 protein 4 196204057_PM_at 3394 IRF8 interferon regulatory factor 8 6.59E−24 89.8184.8 134.9 71.4 197 208296_PM_x_at 25816 TNFAIP8 tumor necrosis factor,alpha-induced 6.65E−24 136.9 242.5 195.1 109.6 protein 8 198204122_PM_at 7305 TYROBP TYRO protein tyrosine kinase binding 6.73E−24190.5 473.4 332.8 143.3 protein 199 224927_PM_at 170954 KIAA1949KIAA1949 6.87E−24 98.8 213.6 160.2 74.7

TABLE 8 Biopsy Expression Profiling of Kidney Transplants: 3-WayClassifier AR vs. ADNR vs. TX (TGCG Samples) p-value Entrez (Final ADNR-AR- # Probeset ID Gene Gene Symbol Gene Title Phenotype) Mean MeanTX-Mean 1 242956_PM_at 3417 IDH1 Isocitrate dehydrogenase 1 (NADP+),soluble 2.95E−22 32.7 29.9 53.6 2 208948_PM_s_at 6780 STAU1 staufen, RNAbinding protein, homolog 1 1.56E−29 1807.9 1531.8 2467.4 (Drosophila) 3213037_PM_x_at 6780 STAU1 staufen, RNA binding protein, homolog 14.77E−27 1699.0 1466.8 2264.9 (Drosophila) 4 207320_PM_x_at 6780 STAU1staufen, RNA binding protein, homolog 1 6.17E−28 1425.1 1194.6 1945.3(Drosophila) 5 1555832_PM_s_at 1316 KLF6 Kruppel-like factor 6 5.82E−23899.4 1076.8 575.1 6 202376_PM_at 12 SERPINA3 serpin peptidaseinhibitor, clade A (alpha-1 1.05E−25 186.2 387.1 51.7 antiproteinase,antitrypsin), member 3 7 226621_PM_at 9180 OSMR oncostatin M receptor1.28E−27 545.6 804.5 312.1 8 218983_PM_at 51279 C1RL complementcomponent 1, r subcomponent- 9.46E−25 167.1 244.5 99.4 like 9215005_PM_at 54550 NECAB2 N-terminal EF-hand calcium binding protein 21.95E−25 36.7 23.4 65.7 10 202720_PM_at 26136 TES testis derivedtranscript (3 LIM domains) 4.32E−24 285.4 379.0 204.2 11 240320_PM_at100131781 C14orf164 chromosome 14 open reading frame 164 1.64E−23 204.984.2 550.0 12 243418_PM_at — — — 1.81E−24 56.3 31.1 104.8 13205466_PM_s_at 9957 HS3ST1 heparan sulfate (glucosamine) 3-O- 5.64E−2473.6 123.8 42.1 sulfotransferase 1 14 201042_PM_at 7052 TGM2transglutaminase 2 (C polypeptide, protein- 3.87E−28 131.8 236.5 80.1glutamine-gamma-glutamyltransferase) 15 202018_PM_s_at 4057 LTFlactotransferrin 4.00E−25 392.3 1332.4 117.7 16 212503_PM_s_at 22982DIP2C DIP2 disco-interacting protein 2 homolog C 6.62E−27 559.7 389.2755.0 (Drosophila) 17 215049_PM_x_at 9332 CD163 CD163 molecule 4.65E−23232.8 481.3 112.9 18 209515_PM_s_at 5873 RAB27A RAB27A, member RASoncogene family 2.11E−23 127.3 192.2 85.2 19 221932_PM_s_at 51218 GLRX5glutaredoxin 5 2.08E−22 1351.5 1145.8 1599.3 20 202207_PM_at 10123 ARL4CADP-ribosylation factor-like 4C 1.83E−29 106.9 258.6 57.2 21227697_PM_at 9021 SOCS3 suppressor of cytokine signaling 3 2.93E−23 35.969.1 19.9 22 227072_PM_at 25914 RTTN rotatin 4.26E−23 101.1 74.5 132.923 201136_PM_at 5355 PLP2 proteolipid protein 2 (colonic epithelium-2.71E−22 187.7 274.0 131.7 enriched) 24 212504_PM_at 22982 DIP2C DIP2disco-interacting protein 2 homolog C 2.57E−25 334.5 227.2 452.5(Drosophila) 25 200986_PM_at 710 SERPING1 serpin peptidase inhibitor,clade G (C1 2.88E−26 442.7 731.5 305.3 inhibitor), member 1 26203233_PM_at 3566 IL4R interleukin 4 receptor 6.30E−23 97.6 138.5 72.027 229295_PM_at 150166 /// IL17RA /// interleukin 17 receptor A ///hypothetical 6.40E−25 76.4 131.8 50.0 23765 LOC150166 protein LOC15016628 231358_PM_at 83876 MRO maestro 1.11E−22 199.7 81.2 422.1 29201666_PM_at 7076 TIMP1 TIMP metallopeptidase inhibitor 1 1.54E−221035.3 1879.4 648.0 30 209774_PM_x_at 2920 CXCL2 chemokine (C-X-C motif)ligand 2 1.50E−26 24.9 52.9 15.5 31 217733_PM_s_at 9168 TMSB10 thymosinbeta 10 9.34E−27 4414.7 6331.3 3529.0 32 222939_PM_s_at 117247 SLC16A10solute carrier family 16, member 10 (aromatic 2.55E−22 156.6 93.7 229.6amino acid transporter) 33 204924_PM_at 7097 TLR2 toll-like receptor 22.11E−22 96.8 162.0 66.6 34 225415_PM_at 151636 DTX3L deltex 3-like(Drosophila) 3.04E−24 230.2 376.4 166.9 35 202206_PM_at 10123 ARL4CADP-ribosylation factor-like 4C 1.16E−22 60.8 128.5 36.0 36213857_PM_s_at 961 CD47 CD47 molecule 2.56E−27 589.6 858.0 481.2 37235529_PM_x_at 25939 SAMHD1 SAM domain and HD domain 1 4.49E−24 189.3379.9 128.0 38 206693_PM_at 3574 IL7 interleukin 7 3.12E−22 37.3 57.028.9 39 219033_PM_at 79668 PARP8 poly (ADP-ribose) polymerase family,member 1.88E−22 47.9 79.2 35.7 8 40 201721_PM_s_at 7805 LAPTM5 lysosomalprotein transmembrane 5 3.72E−24 396.5 934.6 249.4 41 204336_PM_s_at10287 RGS19 regulator of G-protein signaling 19 3.52E−25 95.2 187.7 67.042 235964_PM_x_at 25939 SAMHD1 SAM domain and HD domain 1 2.45E−23 172.9345.9 117.5 43 208091_PM_s_at 81552 VOPP1 vesicular, overexpressed incancer, prosurvival 9.47E−25 495.5 713.9 409.7 protein 1 44204446_PM_s_at 240 ALOX5 arachidonate 5-lipoxygenase 6.25E−31 91.9 323.954.7 45 212703_PM_at 83660 TLN2 talin 2 6.13E−23 270.8 159.7 357.5 46213414_PM_s_at 6223 RPS19 ribosomal protein S19 1.57E−22 4508.2 5432.14081.7 47 1565681_PM_s_at 22982 DIP2C DIP2 disco-interacting protein 2homolog C 2.57E−22 66.4 35.7 92.5 (Drosophila) 48 225764_PM_at 2120 ETV6ets variant 6 2.63E−23 92.6 133.0 77.0 49 227184_PM_at 5724 PTAFRplatelet-activating factor receptor 1.73E−24 89.8 191.4 62.7 50221840_PM_at 5791 PTPRE protein tyrosine phosphatase, receptor type, E8.52E−23 71.0 133.2 51.8 51 225799_PM_at 112597 /// LOC541471hypothetical LOC541471 /// non-protein 1.18E−23 230.5 444.3 172.0 541471/// coding RNA 152 NCRNA00152 52 202957_PM_at 3059 HCLS1 hematopoieticcell-specific Lyn substrate 1 1.94E−25 119.2 299.5 82.2 53 229383_PM_at55016 1-Mar membrane-associated ring finger (C3HC4) 1 8.09E−25 33.8 88.022.9 54 33304_PM_at 3669 ISG20 interferon stimulated exonuclease gene 20kDa 2.83E−27 33.2 101.3 22.1 55 222062_PM_at 9466 IL27RA interleukin 27receptor, alpha 1.79E−22 34.9 65.8 26.4 56 219574_PM_at 55016 1-Marmembrane-associated ring finger (C3HC4) 1 6.65E−25 51.5 126.0 36.2 57202748_PM_at 2634 GBP2 guanylate binding protein 2, interferon- 7.51E−26196.7 473.0 141.0 inducible 58 210895_PM_s_at 942 CD86 CD86 molecule3.80E−23 32.3 85.0 21.6 59 202208_PM_s_at 10123 ARL4C ADP-ribosylationfactor-like 4C 1.24E−23 54.0 99.6 42.2 60 221666_PM_s_at 29108 PYCARDPYD and CARD domain containing 5.55E−24 60.7 132.8 44.7 61 227125_PM_at3455 IFNAR2 interferon (alpha, beta and omega) receptor 2 3.57E−24 70.0126.2 55.8 62 226525_PM_at 9262 STK17B serine/threonine kinase 17b3.43E−24 146.8 338.7 107.6 63 210644_PM_s_at 3903 LAIR1leukocyte-associated immunoglobulin-like 2.96E−24 29.7 74.6 21.2receptor 1 64 230391_PM_at 8832 CD84 CD84 molecule 1.89E−22 47.9 130.132.5 65 242907_PM_at — — — 2.54E−22 59.3 165.1 39.8 66 1553906_PM_s_at221472 FGD2 FYVE, RhoGEF and PH domain containing 2 1.67E−26 104.6 321.071.9 67 223922_PM_x_at 64231 MS4A6A membrane-spanning 4-domains,subfamily A, 8.68E−24 289.2 656.8 214.5 member 6A 68 230550_PM_at 64231MS4A6A membrane-spanning 4-domains, subfamily A, 7.63E−24 44.8 124.330.9 member 6A 69 202953_PM_at 713 C1QB complement component 1, qsubcomponent, B 2.79E−22 215.8 638.4 142.5 chain 70 213733_PM_at 4542MYO1F myosin IF 2.25E−23 26.8 52.7 20.9 71 204774_PM_at 2123 EVI2Aecotropic viral integration site 2A 5.10E−24 63.6 168.5 44.9 72211366_PM_x_at 834 CASP1 caspase 1, apoptosis-related cysteine 3.40E−24115.3 261.0 87.1 peptidase (interleukin 1, beta, convertase) 73204698_PM_at 3669 ISG20 interferon stimulated exonuclease gene 20 kDa1.27E−28 41.5 165.1 27.6 74 201762_PM_s_at 5721 PSME2 proteasome(prosome, macropain) activator 2.31E−22 1251.6 1825.1 1091.0 subunit 2(PA28 beta) 75 204470_PM_at 2919 CXCL1 chemokine (C-X-C motif) ligand 1(melanoma 2.81E−24 22.9 63.7 16.2 growth stimulating activity, alpha) 76242827_PM_x_at — — — 9.00E−23 22.1 52.3 16.3 77 209970_PM_x_at 834 CASP1caspase 1, apoptosis-related cysteine 4.40E−25 116.0 266.6 88.7peptidase (interleukin 1, beta, convertase) 78 228532_PM_at 128346C1orf162 chromosome 1 open reading frame 162 1.57E−22 82.6 217.7 60.0 79232617_PM_at 1520 CTSS cathepsin S 2.83E−23 209.6 537.9 154.8 80203761_PM_at 6503 SLA Src-like-adaptor 3.80E−23 69.6 179.2 51.4 81219666_PM_at 64231 MS4A6A membrane-spanning 4-domains, subfamily A,4.29E−23 159.2 397.6 118.7 member 6A 82 223280_PM_x_at 64231 MS4A6Amembrane-spanning 4-domains, subfamily A, 2.53E−24 269.0 711.8 199.5member 6A 83 225701_PM_at 80709 AKNA AT-hook transcription factor2.87E−29 37.7 102.8 29.0 84 224356_PM_x_at 64231 MS4A6Amembrane-spanning 4-domains, subfamily A, 1.56E−23 150.6 399.6 111.2member 6A 85 202643_PM_s_at 7128 TNFAIP3 tumor necrosis factor,alpha-induced protein 3 9.92E−24 43.7 92.8 34.8 86 202644_PM_s_at 7128TNFAIP3 tumor necrosis factor, alpha-induced protein 3 2.12E−27 169.8380.4 136.6 87 213566_PM_at 6039 RNASE6 ribonuclease, RNase A family, k61.55E−23 180.9 482.0 134.3 88 219386_PM_s_at 56833 SLAMF8 SLAM familymember 8 1.22E−22 28.2 92.1 19.4 89 203416_PM_at 963 CD53 CD53 molecule3.13E−23 215.9 603.0 157.8 90 200003_PM_s_at 6158 RPL28 ribosomalprotein L28 1.73E−22 4375.2 5531.2 4069.9 91 206420_PM_at 10261 IGSF6immunoglobulin superfamily, member 6 5.65E−23 45.1 131.5 32.7 92217028_PM_at 7852 CXCR4 chemokine (C-X-C motif) receptor 4 6.84E−27100.8 304.4 75.3 93 232024_PM_at 26157 GIMAP2 GTPase, IMAP family member2 2.94E−24 90.2 197.7 72.5 94 238327_PM_at 440836 ODF3B outer densefiber of sperm tails 3B 1.75E−27 32.8 81.4 26.1 95 209083_PM_at 11151CORO1A coronin, actin binding protein, 1A 2.78E−27 46.9 163.8 34.3 96232724_PM_at 64231 MS4A6A membrane-spanning 4-domains, subfamily A,6.28E−23 24.8 47.6 20.7 member 6A 97 211742_PM_s_at 2124 EVI2B ecotropicviral integration site 2B 1.56E−22 73.9 236.5 53.2 98 202659_PM_at 5699PSMB10 proteasome (prosome, macropain) subunit, 2.29E−24 180.7 355.6151.5 beta type, 10 99 226991_PM_at 4773 NFATC2 nuclear factor ofactivated T-cells, cytoplasmic, 2.42E−23 37.9 87.0 30.3calcineurin-dependent 2 100 210538_PM_s_at 330 BIRC3 baculoviral IAPrepeat-containing 3 5.40E−26 106.7 276.7 84.6 101 205269_PM_at 3937 LCP2lymphocyte cytosolic protein 2 (SH2 domain 7.35E−25 30.2 92.2 22.9containing leukocyte protein of 76 kDa) 102 211368_PM_s_at 834 CASP1caspase 1, apoptosis-related cysteine 9.43E−27 102.6 274.3 81.8peptidase (interleukin 1, beta, convertase) 103 205798_PM_at 3575 IL7Rinterleukin 7 receptor 1.57E−24 44.1 136.8 33.4 104 228442_PM_at 4773NFATC2 nuclear factor of activated T-cells, cytoplasmic, 5.58E−23 39.084.8 32.0 calcineurin-dependent 2 105 213603_PM_s_at 5880 RAC2ras-related C3 botulinum toxin substrate 2 (rho 3.38E−25 113.9 366.586.5 family, small GTP binding protein Rac2) 106 228964_PM_at 639 PRDM1PR domain containing 1, with ZNF domain 1.42E−23 21.2 52.1 17.0 107209606_PM_at 9595 CYTIP cytohesin 1 interacting protein 1.66E−26 41.4114.3 32.9 108 214022_PM_s_at 8519 IFITM1 interferon inducedtransmembrane protein 1 1.61E−23 799.1 1514.7 683.3 (9-27) 109202307_PM_s_at 6890 TAP1 transporter 1, ATP-binding cassette, sub-family8.61E−26 172.4 420.6 141.0 B (MDR/TAP) 110 204882_PM_at 9938 ARHGAP25Rho GTPase activating protein 25 8.14E−23 51.5 107.3 43.0 111227344_PM_at 10320 IKZF1 IKAROS family zinc finger 1 (Ikaros) 5.30E−2617.8 40.0 14.9 112 205270_PM_s_at 3937 LCP2 lymphocyte cytosolic protein2 (SH2 domain 3.75E−24 56.8 162.6 44.6 containing leukocyte protein of76 kDa) 113 223640_PM_at 10870 HCST hematopoietic cell signal transducer5.55E−23 91.0 234.0 72.7 114 226218_PM_at 3575 IL7R interleukin 7receptor 2.15E−23 55.6 197.0 41.4 115 226219_PM_at 257106 ARHGAP30 RhoGTPase activating protein 30 1.87E−26 46.4 127.9 37.5 116 38149_PM_at9938 ARHGAP25 Rho GTPase activating protein 25 1.20E−23 51.3 108.9 43.2117 213975_PM_s_at 4069 LYZ lysozyme 2.70E−22 458.4 1626.0 338.1 118238668_PM_at — — — 1.35E−23 18.2 49.2 14.5 119 200887_PM_s_at 6772 STAT1signal transducer and activator of transcription 1.27E−22 1141.7 2278.6972.9 1, 91 kDa 120 1555756_PM_a_at 64581 CLEC7A C-type lectin domainfamily 7, member A 2.11E−22 16.6 45.4 13.2 121 205039_PM_s_at 10320IKZF1 IKAROS family zinc finger 1 (Ikaros) 6.73E−23 29.1 65.9 24.2 122206011_PM_at 834 CASP1 caspase 1, apoptosis-related cysteine 6.34E−2574.5 198.0 60.7 peptidase (interleukin 1, beta, convertase) 123221698_PM_s_at 64581 CLEC7A C-type lectin domain family 7, member A9.79E−24 61.5 164.6 49.7 124 227346_PM_at 10320 IKZF1 IKAROS family zincfinger 1 (Ikaros) 1.51E−26 24.9 79.7 19.8 125 230499_PM_at — — —8.55E−23 29.7 68.7 24.7 126 229391_PM_s_at 441168 FAM26F family withsequence similarity 26, member F 2.74E−23 98.6 379.7 73.7 127205159_PM_at 1439 CSF2RB colony stimulating factor 2 receptor, beta,low- 1.74E−23 33.7 107.5 26.3 affinity (granulocyte-macrophage) 128205639_PM_at 313 AOAH acyloxyacyl hydrolase (neutrophil) 4.43E−23 30.372.6 25.2 129 204563_PM_at 6402 SELL selectin L 1.00E−23 40.7 134.7 31.7130 201288_PM_at 397 ARHGDIB Rho GDP dissociation inhibitor (GDI) beta1.92E−22 354.9 686.8 308.1 131 209969_PM_s_at 6772 STAT1 signaltransducer and activator of transcription 5.49E−24 395.8 1114.5 320.8 1,91 kDa 132 229390_PM_at 441168 FAM26F family with sequence similarity26, member F 3.91E−25 103.8 520.0 75.9 133 242916_PM_at 11064 CEP110centrosomal protein 110 kDa 1.99E−23 30.8 68.1 26.2 134 207651_PM_at29909 GPR171 G protein-coupled receptor 171 1.90E−29 25.8 89.9 20.9 135229937_PM_x_at 10859 LILRB1 Leukocyte immunoglobulin-like receptor,1.67E−24 23.5 79.9 18.5 subfamily B (with TM and ITIM domains), member136 232543_PM_x_at 64333 ARHGAP9 Rho GTPase activating protein 93.66E−26 31.7 99.1 25.8 137 203332_PM_s_at 3635 INPP5D inositolpolyphosphate-5-phosphatase, 145 kDa 3.66E−24 27.9 60.5 24.0 138213160_PM_at 1794 DOCK2 dedicator of cytokinesis 2 1.03E−24 33.7 92.627.8 139 204912_PM_at 3587 IL10RA interleukin 10 receptor, alpha1.28E−24 57.0 178.7 46.1 140 204205_PM_at 60489 APOBEC3G apolipoproteinB mRNA editing enzyme, 6.40E−27 95.4 289.4 78.7 catalyticpolypeptide-like 3G 141 206513_PM_at 9447 AIM2 absent in melanoma 29.67E−23 17.2 50.7 13.9 142 203741_PM_s_at 113 ADCY7 adenylate cyclase 72.34E−22 23.6 59.3 19.7 143 206118_PM_at 6775 STAT4 signal transducerand activator of transcription 8.28E−26 21.0 49.5 18.1 4 144227677_PM_at 3718 JAK3 Janus kinase 3 6.48E−24 18.6 51.7 15.5 145227353_PM_at 147138 TMC8 transmembrane channel-like 8 6.49E−29 19.8 64.216.6 146 1552701_PM_a_at 114769 CARD16 caspase recruitment domainfamily, member 2.52E−23 143.8 413.6 119.8 16 147 1552703_PM_s_at 114769/// CARD16 /// caspase recruitment domain family, member 7.57E−24 64.6167.8 54.9 834 CASP1 16 /// caspase 1, apoptosis-related cysteine 148229437_PM_at 114614 MIR155HG MIR155 host gene (non-protein coding)3.90E−27 15.4 50.4 12.9 149 204319_PM_s_at 6001 RGS10 regulator ofG-protein signaling 10 6.54E−24 159.7 360.4 139.4 150 204118_PM_at 962CD48 CD48 molecule 3.85E−23 82.9 341.5 65.2 151 1559584_PM_a_at 283897C16orf54 chromosome 16 open reading frame 54 2.86E−24 31.3 95.8 26.1 152212588_PM_at 5788 PTPRC protein tyrosine phosphatase, receptor type, C2.46E−22 94.4 321.0 76.4 153 219014_PM_at 51316 PLAC8 placenta-specific8 2.03E−23 38.8 164.1 30.7 154 235735_PM_at — — — 1.39E−25 13.0 34.911.2 155 203932_PM_at 3109 HLA-DMB major histocompatibility complex,class II, DM 6.25E−23 422.4 853.9 376.0 beta 156 223502_PM_s_at 10673INFSF13B tumor necrosis factor (ligand) superfamily, 2.62E−23 73.0 244.360.0 member 13b 157 1405_PM_i_at 6352 CCL5 chemokine (C-C motif) ligand5 2.22E−25 68.0 295.7 54.6 158 226474_PM_at 84166 NLRC5 NLR family, CARDdomain containing 5 1.33E−23 64.4 173.5 55.1 159 204220_PM_at 9535 GMFGglia maturation factor, gamma 1.56E−23 147.0 339.3 128.9 160204923_PM_at 54440 SASH3 SAM and SH3 domain containing 3 4.56E−23 25.468.2 21.7 161 206082_PM_at 10866 HCP5 HLA complex P5 1.23E−23 76.2 185.166.6 162 204670_PM_x_at 3123 /// HLA-DRB1 /// major histocompatibilitycomplex, class II, DR 1.31E−23 2461.9 4344.6 2262.0 3126 HLA-DRB4 beta 1/// major histocompatibility comp 163 228869_PM_at 124460 SNX20 sortingnexin 20 2.59E−22 25.7 67.3 22.2 164 205831_PM_at 914 CD2 CD2 molecule2.30E−27 40.4 162.5 33.9 165 206978_PM_at 729230 CCR2 chemokine (C-Cmotif) receptor 2 4.46E−23 32.1 100.7 27.3 166 224451_PM_x_at 64333ARHGAP9 Rho GTPase activating protein 9 4.23E−24 34.2 103.4 29.4 167204279_PM_at 5698 PSMB9 proteasome (prosome, macropain) subunit,2.81E−23 241.6 637.4 211.3 beta type, 9 (large multifunctional peptidase168 209795_PM_at 969 CD69 CD69 molecule 5.81E−27 17.6 57.6 15.2 169229625_PM_at 115362 GBPS guanylate binding protein 5 5.85E−24 29.3 133.324.0 170 213416_PM_at 3676 ITGA4 integrin, alpha 4 (antigen CD49D, alpha4 1.19E−23 26.3 78.1 22.8 subunit of VLA-4 receptor) 171 206804_PM_at917 CD3G CD3g molecule, gamma (CD3-TCR complex) 1.50E−27 19.7 60.6 17.3172 222895_PM_s_at 64919 BCL11B B-cell CLL/lymphoma 11B (zinc fingerprotein) 7.01E−23 22.0 64.5 19.1 173 211582_PM_x_at 7940 LST1 leukocytespecific transcript 1 2.13E−22 57.5 183.8 49.4 174 1555852_PM_at100507463 LOC10050746 hypothetical LOC100507463 8.92E−26 78.9 202.6 70.93 175 213539_PM_at 915 CD3D CD3d molecule, delta (CD3-TCR complex)9.01E−27 70.8 335.1 60.0 176 239237_PM_at — — — 9.82E−24 15.9 34.9 14.5177 229723_PM_at 117289 TAGAP T-cell activation RhoGTPase activatingprotein 5.21E−24 29.2 82.9 26.2 178 204655_PM_at 6352 CCL5 chemokine(C-C motif) ligand 5 7.63E−24 77.5 339.4 66.8 179 229041_PM_s_at — — —1.64E−24 36.5 132.1 32.4 180 205267_PM_at 5450 POU2AF1 POU class 2associating factor 1 4.47E−23 17.9 86.8 15.7 181 226878_PM_at 3111HLA-DOA major histocompatibility complex, class II, DO 2.59E−25 102.0288.9 94.3 alpha 182 205488_PM_at 3001 GZMA granzyme A (granzyme 1,cytotoxic T- 4.14E−25 37.3 164.8 33.4 lymphocyte-associated serineesterase 3) 183 201137_PM_s_at 3115 HLA-DPB1 major histocompatibilitycomplex, class II, DP 2.17E−22 1579.1 3863.7 1475.9 beta 1 184204891_PM_s_at 3932 LCK lymphocyte-specific protein tyrosine kinase7.95E−28 19.3 74.2 17.8 185 231776_PM_at 8320 EOMES eomesodermin1.71E−23 24.0 63.9 22.4 186 211339_PM_s_at 3702 ITK IL2-inducible T-cellkinase 7.40E−23 16.7 44.4 15.7 187 223322_PM_at 83593 RASSF5 Rasassociation (RaIGDS/AF-6) domain family 5.21E−26 41.5 114.4 39.2 member5 188 205758_PM_at 925 CD8A CD8a molecule 2.80E−25 24.2 105.8 22.2 189231124_PM_x_at 4063 LY9 lymphocyte antigen 9 2.81E−23 16.7 45.8 15.7 190211796_PM_s_at 28638 /// TRBC1 /// T cell receptor beta constant 1 /// Tcell 4.38E−25 69.2 431.5 63.6 28639 TRBC2 receptor beta constant 2 191210915_PM_x_at 28638 TRBC2 T cell receptor beta constant 2 1.91E−27 39.7230.7 37.5 192 205821_PM_at 22914 KLRK1 killer cell lectin-like receptorsubfamily K, 1.18E−25 30.3 111.5 31.3 member 1 193 236295_PM_s_at 197358NLRC3 NLR family, CARD domain containing 3 6.22E−26 19.0 52.5 18.6 194213193_PM_x_at 28639 TRBC1 T cell receptor beta constant 1 4.22E−25 95.3490.9 92.1 195 211656_PM_x_at 100133583 HLA-DQB1 /// majorhistocompatibility complex, class II, DQ 5.98E−25 211.3 630.2 208.2 ///3119 LOC10013358 beta 1 /// HLA class II histocompatibili 3 196209670_PM_at 28755 TRAC T cell receptor alpha constant 1.44E−24 37.9149.9 38.5 197 213888_PM_s_at 80342 TRAF3IP3 TRAF3 interacting protein 31.66E−22 30.8 101.9 30.5

TABLE 9 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN vs. TX p-value Entrez (Final CAN- TX- # Probeset ID GeneGene Symbol Gene Title Phenotype) Mean Mean 1 204698_PM_at 3669 ISG20interferon stimulated exonuclease gene 20 kDa 1.93E−19 96.1 27.6 233304_PM_at 3669 ISG20 interferon stimulated exonuclease gene 20 kDa2.02E−19 63.4 22.1 3 217022_PM_s_at 100126583 IGHA1 /// immunoglobulinheavy constant alpha 1 /// 4.31E−19 494.6 49.4 /// 3493 /// IGHA2 ///immunoglobulin heavy constant alpha 2 (A2m ma 3494 LOC10012658 3 4202957_PM_at 3059 HCLS1 hematopoietic cell-specific Lyn substrate 15.13E−19 229.9 82.2 5 203761_PM_at 6503 SLA Src-like-adaptor 1.10E−18138.4 51.4 6 204446_PM_s_at 240 ALOX5 arachidonate 5-lipoxygenase1.36E−18 216.7 54.7 7 209198_PM_s_at 23208 SYT11 synaptotagmin XI1.93E−18 41.8 22.8 8 228964_PM_at 639 PRDM1 PR domain containing 1, withZNF domain 2.37E−18 41.5 17.0 9 201042_PM_at 7052 TGM2 transglutaminase2 (C polypeptide, protein-glutamine- 3.13E−18 172.6 80.1gamma-glutamyltransferase) 10 226219_PM_at 257106 ARHGAP30 Rho GTPaseactivating protein 30 7.21E−18 91.8 37.5 11 225701_PM_at 80709 AKNAAT-hook transcription factor 7.27E−18 73.2 29.0 12 202207_PM_at 10123ARL4C ADP-ribosylation factor-like 4C 7.98E−18 190.4 57.2 13219574_PM_at 55016 MAR1 membrane-associated ring finger (C3HC4) 18.98E−18 87.5 36.2 14 209083_PM_at 12-Jul CORO1A coronin, actin bindingprotein, 1A 1.06E−17 107.1 34.3 15 226621_PM_at 9180 OSMR oncostatin Mreceptor 1.85E−17 682.9 312.1 16 1405_PM_i_at 6352 CCL5 chemokine (C-Cmotif) ligand 5 2.19E−17 195.6 54.6 17 213160_PM_at 1794 DOCK2 dedicatorof cytokinesis 2 2.75E−17 66.0 27.8 18 227346_PM_at 10320 IKZF1 IKAROSfamily zinc finger 1 (Ikaros) 2.92E−17 53.3 19.8 19 204205_PM_at 60489APOBEC3G apolipoprotein B mRNA editing enzyme, catalytic 2.92E−17 192.078.7 polypeptide-like 3G 20 218322_PM_s_at 51703 ACSL5 acyl-CoAsynthetase long-chain family member 5 3.15E−17 84.5 48.1 21 238327_PM_at440836 ODF3B outer dense fiber of sperm tails 3B 3.42E−17 58.5 26.1 22218983_PM_at 51279 C1RL complement component 1, r subcomponent-like4.33E−17 206.9 99.4 23 210538_PM_s_at 330 BIRC3 baculoviral IAPrepeat-containing 3 4.49E−17 199.1 84.6 24 207651_PM_at 29909 GPR171 Gprotein-coupled receptor 171 5.48E−17 57.0 20.9 25 201601_PM_x_at 8519IFITM1 interferon induced transmembrane protein 1 (9-27) 6.07E−17 2202.51251.1 26 226878_PM_at 3111 HLA-DOA major histocompatibility complex,class II, DO alpha 6.12E−17 201.4 94.3 27 1555756_PM_a_at 64581 CLEC7AC-type lectin domain family 7, member A 6.22E−17 28.9 13.2 281559584_PM_a_at 283897 C16orf54 chromosome 16 open reading frame 546.73E−17 71.5 26.1 29 209795_PM_at 969 CD69 CD69 molecule 9.46E−17 40.615.2 30 230550_PM_at 64231 MS4A6A membrane-spanning 4-domains, subfamilyA, member 6A 1.20E−16 88.0 30.9 31 1553906_PM_s_at 221472 FGD2 FYVE,RhoGEF and PH domain containing 2 1.34E−16 219.3 71.9 32 205798_PM_at3575 IL7R interleukin 7 receptor 1.55E−16 106.3 33.4 33 1555852_PM_at100507463 LOC10050746 hypothetical LOC100507463 1.81E−16 154.2 70.9 3 34224916_PM_at 340061 TMEM173 transmembrane protein 173 1.84E−16 67.8 40.035 211368_PM_s_at 834 CASP1 caspase 1, apoptosis-related cysteinepeptidase 1.85E−16 191.4 81.8 (interleukin 1, beta, convertase) 36226474_PM_at 84166 NLRC5 NLR family, CARD domain containing 5 1.85E−16129.8 55.1 37 201137_PM_s_at 3115 HLA-DPB1 major histocompatibilitycomplex, class II, DP beta 1 1.90E−16 3151.7 1475.9 38 210785_PM_s_at9473 C1orf38 chromosome 1 open reading frame 38 2.07E−16 39.9 16.4 39215121_PM_x_at 100290481 IGLC7 /// immunoglobulin lambda constant 7 ///immunoglobulin 2.13E−16 1546.8 250.4 /// 28823 IGLV1-44 /// lambdavariable 1-44 /// immunoglob /// 28834 LOC10029048 1 40 1555832_PM_s_at1316 KLF6 Kruppel-like factor 6 2.35E−16 1003.3 575.1 41 221932_PM_s_at51218 GLRX5 glutaredoxin 5 2.49E−16 1218.1 1599.3 42 207677_PM_s_at 4689NCF4 neutrophil cytosolic factor 4, 40 kDa 2.65E−16 39.5 19.2 43202720_PM_at 26136 TES testis derived transcript (3 LIM domains)2.68E−16 357.9 204.2 44 220005_PM_at 53829 P2RY13 purinergic receptorP2Y, G-protein coupled, 13 2.72E−16 29.6 14.8 45 200904_PM_at 3133 HLA-Emajor histocompatibility complex, class I, E 2.73E−16 1607.7 994.7 46222294_PM_s_at 5873 RAB27A RAB27A, member RAS oncogene family 2.91E−16263.0 146.4 47 205831_PM_at 914 CD2 CD2 molecule 3.32E−16 100.9 33.9 48227344_PM_at 10320 IKZF1 IKAROS family zinc finger 1 (Ikaros) 3.39E−1628.5 14.9 49 209374_PM_s_at 3507 IGHM immunoglobulin heavy constant mu3.73E−16 301.0 45.0 50 202307_PM_s_at 6890 TAP1 transporter 1,ATP-binding cassette, sub-family B 4.84E−16 280.0 141.0 (MDR/TAP) 51223218_PM_s_at 64332 NFKBIZ nuclear factor of kappa light polypeptidegene enhancer in 5.05E−16 399.9 159.1 B-cells inhibitor, zeta 52229437_PM_at 114614 MIR155HG MIR155 host gene (non-protein coding)5.85E−16 28.5 12.9 53 213603_PM_s_at 5880 RAC2 ras-related C3 botulinumtoxin substrate 2 (rho family, 5.98E−16 250.3 86.5 small GTP bindingprotein Rac2) 54 214669_PM_x_at 3514 /// IGK@ /// immunoglobulin kappalocus /// immunoglobulin kappa 6.32E−16 3449.1 587.1 50802 IGKC constant55 211430_PM_s_at 28396 /// IGHG1 /// immunoglobulin heavy constantgamma 1 (G1m marker) 6.39E−16 2177.7 266.9 3500 /// IGHM /// ///immunoglobulin heavy constant mu 3507 IGHV4-31 56 228471_PM_at 91526ANKRD44 ankyrin repeat domain 44 6.42E−16 184.6 86.5 57 209138_PM_x_at3535 IGL@ Immunoglobulin lambda locus 7.54E−16 2387.0 343.2 58227353_PM_at 147138 TMC8 transmembrane channel-like 8 8.01E−16 42.7 16.659 200986_PM_at 710 SERPING1 serpin peptidase inhibitor, clade G (C1inhibitor), member 8.10E−16 590.2 305.3 1 60 212203_PM_x_at 10410 IFITM3interferon induced transmembrane protein 3 (1-8U) 8.17E−16 4050.1 2773.861 221651_PM_x_at 3514 /// IGK@ /// immunoglobulin kappa locus ///immunoglobulin kappa 9.60E−16 3750.2 621.2 50802 IGKC constant 62214836_PM_x_at 28299 /// IGK@ /// immunoglobulin kappa locus ///immunoglobulin kappa 9.72E−16 544.2 109.1 3514 /// IGKC /// constant ///immunoglobulin kappa v 50802 IGKV1-5 63 1552703_PM_s_at 114769 ///CARD16 /// caspase recruitment domain family, member 16 /// 1.12E−15120.6 54.9 834 CASP1 caspase 1, apoptosis-related cysteine 64202901_PM_x_at 1520 CTSS cathepsin S 1.13E−15 130.9 45.1 65215379_PM_x_at 28823 /// IGLC7 /// immunoglobulin lambda constant 7 ///immunoglobulin 1.16E−15 1453.0 248.1 28834 IGLV1-44 lambda variable 1-4466 222939_PM_s_at 117247 SLC16A10 solute carrier family 16, member 10(aromatic amino acid 1.22E−15 115.1 229.6 transporter) 67 232617_PM_at1520 CTSS cathepsin S 1.22E−15 392.2 154.8 68 235964_PM_x_at 25939SAMHD1 SAM domain and HD domain 1 1.26E−15 270.1 117.5 69 205159_PM_at1439 CSF2RB colony stimulating factor 2 receptor, beta, low-affinity1.28E−15 70.4 26.3 (granulocyte-macrophage) 70 224451_PM_x_at 64333ARHGAP9 Rho GTPase activating protein 9 1.34E−15 71.8 29.4 71214677_PM_x_at 100287927 IGL@ /// Immunoglobulin lambda locus ///Hypothetical protein 1.35E−15 2903.1 433.7 /// 3535 LOC10028792LOC100287927 7 72 217733_PM_s_at 9168 TMSB10 thymosin beta 10 1.37E−155555.2 3529.0 73 38149_PM_at 9938 ARHGAP25 Rho GTPase activating protein25 1.46E−15 83.2 43.2 74 221671_PM_x_at 3514 /// IGK@ /// immunoglobulinkappa locus /// immunoglobulin kappa 1.57E−15 3722.5 642.9 50802 IGKCconstant 75 214022_PM_s_at 8519 IFITM1 interferon induced transmembraneprotein 1 (9-27) 1.59E−15 1236.6 683.3 76 223217_PM_s_at 64332 NFKBIZnuclear factor of kappa light polypeptide gene enhancer in 1.61E−15196.6 79.7 B-cells inhibitor, zeta 77 206118_PM_at 6775 STAT4 signaltransducer and activator of transcription 4 1.67E−15 37.7 18.1 78221666_PM_s_at 29108 PYCARD PYD and CARD domain containing 1.82E−15 95.544.7 79 207375_PM_s_at 3601 IL15RA interleukin 15 receptor, alpha1.94E−15 51.2 28.2 80 209197_PM_at 23208 SYT11 synaptotagmin XI 2.02E−1538.2 24.9 81 243366_PM_s_at — — — 2.05E−15 52.5 22.0 82 224795_PM_x_at3514 /// IGK@ /// immunoglobulin kappa locus /// immunoglobulin kappa2.18E−15 3866.2 670.5 50802 IGKC constant 83 36711_PM_at 23764 MAFFv-maf musculoaponeurotic fibrosarcoma oncogene 2.26E−15 113.0 40.7homolog F (avian) 84 227125_PM_at 3455 IFNAR2 interferon (alpha, betaand omega) receptor 2 2.27E−15 96.7 55.8 85 235735_PM_at — — — 2.58E−1524.5 11.2 86 209515_PM_s_at 5873 RAB27A RAB27A, member RAS oncogenefamily 2.61E−15 160.5 85.2 87 204670_PM_x_at 3123 /// HLA-DRB1 /// majorhistocompatibility complex, class II, DR beta 1 /// 2.61E−15 3694.92262.0 3126 HLA-DRB4 major histocompatibility comp 88 205269_PM_at 3937LCP2 lymphocyte cytosolic protein 2 (SH2 domain containing 2.85E−15 58.922.9 leukocyte protein of 76 kDa) 89 226525_PM_at 9262 STK17Bserine/threonine kinase 17b 3.00E−15 259.1 107.6 90 229295_PM_at 150166/// IL17RA /// interleukin 17 receptor A /// hypothetical protein3.02E−15 98.4 50.0 23765 LOC150166 LOC150166 91 206513_PM_at 9447 AIM2absent in melanoma 2 3.18E−15 30.5 13.9 92 209774_PM_x_at 2920 CXCL2chemokine (C-X-C motif) ligand 2 3.45E−15 38.2 15.5 93 211656_PM_x_at100133583 HLA-DQB1 /// major histocompatibility complex, class II, DQbeta 1 /// 3.51E−15 459.8 208.2 /// 3119 LOC10013358 HLA class IIhistocompatibili 3 94 206011_PM_at 834 CASP1 caspase 1,apoptosis-related cysteine peptidase 3.56E−15 146.2 60.7 (interleukin 1,beta, convertase) 95 202803_PM_s_at 3689 ITGB2 integrin, beta 2(complement component 3 receptor 3 and 3.68E−15 182.6 65.2 4 subunit) 96221698_PM_s_at 64581 CLEC7A C-type lectin domain family 7, member A3.69E−15 112.8 49.7 97 229937_PM_x_at 10859 LILRB1 Leukocyteimmunoglobulin-like receptor, subfamily B 3.75E−15 50.0 18.5 (with TMand ITIM domains), member 98 235529_PM_x_at 25939 SAMHD1 SAM domain andHD domain 1 3.99E−15 289.1 128.0 99 223322_PM_at 83593 RASSF5 Rasassociation (RaIGDS/AF-6) domain family member 5 4.03E−15 79.3 39.2 100211980_PM_at 1282 COL4A1 collagen, type IV, alpha 1 4.75E−15 1295.8774.7 101 201721_PM_s_at 7805 LAPTM5 lysosomal protein transmembrane 54.83E−15 661.3 249.4 102 242916_PM_at 11064 CEP110 centrosomal protein110 kDa 4.89E−15 51.2 26.2 103 206978_PM_at 729230 CCR2 chemokine (C-Cmotif) receptor 2 5.01E−15 68.6 27.3 104 244353_PM_s_at 154091 SLC2A12solute carrier family 2 (facilitated glucose transporter), 5.72E−15 51.6100.5 member 12 105 215049_PM_x_at 9332 CD163 CD163 molecule 6.21E−15344.5 112.9 106 1552510_PM_at 142680 SLC34A3 solute carrier family 34(sodium phosphate), member 3 6.40E−15 95.6 206.6 107 225636_PM_at 6773STAT2 signal transducer and activator of transcription 2, 113 kDa6.63E−15 711.5 485.3 108 229390_PM_at 441168 FAM26F family with sequencesimilarity 26, member F 6.73E−15 272.4 75.9 109 235229_PM_at — — —6.90E−15 135.0 41.9 110 226218_PM_at 3575 IL7R interleukin 7 receptor7.22E−15 147.1 41.4 111 217028_PM_at 7852 CXCR4 chemokine (C-X-C motif)receptor 4 7.40E−15 208.4 75.3 112 204655_PM_at 6352 CCL5 chemokine (C-Cmotif) ligand 5 8.57E−15 223.4 66.8 113 227184_PM_at 5724 PTAFRplatelet-activating factor receptor 8.78E−15 134.4 62.7 114 202748_PM_at2634 GBP2 guanylate binding protein 2, interferon-inducible 8.91E−15306.7 141.0 115 226991_PM_at 4773 NFATC2 nuclear factor of activatedT-cells, cytoplasmic, 9.05E−15 66.7 30.3 calcineurin-dependent 2 116216565_PM_x_at — — — 9.49E−15 1224.6 779.1 117 203104_PM_at 1436 CSF1Rcolony stimulating factor 1 receptor 9.57E−15 42.7 22.1 118 238668_PM_at— — — 9.84E−15 33.6 14.5 119 204923_PM_at 54440 SASH3 SAM and SH3 domaincontaining 3 9.93E−15 47.6 21.7 120 230036_PM_at 219285 SAMD9L sterilealpha motif domain containing 9-like 1.02E−14 128.0 72.7 121211742_PM_s_at 2124 EVI2B ecotropic viral integration site 2B 1.03E−14166.2 53.2 122 236782_PM_at 154075 SAMD3 sterile alpha motif domaincontaining 3 1.11E−14 23.3 13.3 123 232543_PM_x_at 64333 ARHGAP9 RhoGTPase activating protein 9 1.13E−14 62.3 25.8 124 231124_PM_x_at 4063LY9 lymphocyte antigen 9 1.18E−14 33.7 15.7 125 215946_PM_x_at 3543 ///IGLL1 /// immunoglobulin lambda-like polypeptide 1 /// 1.22E−14 187.552.1 91316 /// IGLL3P /// immunoglobulin lambda-like polypeptide 3,91353 LOC91316 126 208306_PM_x_at 3123 HLA-DRB1 Major histocompatibilitycomplex, class II, DR beta 1 1.25E−14 3695.8 2255.0 127 217235_PM_x_at28816 IGLV2-11 immunoglobulin lambda variable 2-11 1.29E−14 196.8 37.8128 209546_PM_s_at 8542 APOL1 apolipoprotein L, 1 1.33E−14 206.8 114.1129 203416_PM_at 963 CD53 CD53 molecule 1.34E−14 422.7 157.8 130211366_PM_x_at 834 CASP1 caspase 1, apoptosis-related cysteine peptidase1.35E−14 186.0 87.1 (interleukin 1, beta, convertase) 131 200797_PM_s_at4170 MCL1 myeloid cell leukemia sequence 1 (BCL2-related) 1.38E−14 793.7575.9 132 31845_PM_at 2000 ELF4 E74-like factor 4 (ets domaintranscription factor) 1.40E−14 60.7 34.3 133 221841_PM_s_at 9314 KLF4Kruppel-like factor 4 (gut) 1.48E−14 132.3 65.2 134 229391_PM_s_at441168 FAM26F family with sequence similarity 26, member F 1.49E−14212.1 73.7 135 203645_PM_s_at 9332 CD163 CD163 molecule 1.51E−14 274.985.0 136 211643_PM_x_at 100510044 IGK@ /// immunoglobulin kappa locus/// immunoglobulin kappa 1.61E−14 131.1 32.6 /// 28875 IGKC /// constant/// immunoglobulin kappa v /// 3514 /// IGKV3D-15 /// 50802 LOC100510044 137 205488_PM_at 3001 GZMA granzyme A (granzyme 1, cytotoxicT-lymphocyte- 1.82E−14 102.3 33.4 associated serine esterase 3) 138201464_PM_x_at 3725 JUN jun proto-oncogene 1.90E−14 424.7 244.5 139204774_PM_at 2123 EVI2A ecotropic viral integration site 2A 1.95E−14114.7 44.9 140 204336_PM_s_at 10287 RGS19 regulator of G-proteinsignaling 19 2.01E−14 135.7 67.0 141 244654_PM_at 64005 MYO1G myosin IG2.03E−14 26.8 14.9 142 228442_PM_at 4773 NFATC2 nuclear factor ofactivated T-cells, cytoplasmic, 2.06E−14 62.8 32.0 calcineurin-dependent2 143 206804_PM_at 917 CD3G CD3g molecule, gamma (CD3-TCR complex)2.18E−14 36.4 17.3 144 201315_PM_x_at 10581 IFITM2 interferon inducedtransmembrane protein 2 (1-8D) 2.21E−14 3303.7 2175.3 145 203561_PM_at2212 FCGR2A Fc fragment of IgG, low affinity IIa, receptor (CD32)2.22E−14 66.4 29.2 146 219117_PM_s_at 51303 FKBP11 FK506 binding protein11, 19 kDa 2.31E−14 341.3 192.9 147 242827_PM_x_at — — — 2.37E−14 38.916.3 148 214768_PM_x_at 28299 /// IGK@ /// immunoglobulin kappa locus/// immunoglobulin kappa 2.38E−14 116.7 21.1 3514 /// IGKC /// constant/// immunoglobulin kappa v 50802 IGKV1-5 149 227253_PM_at 1356 CPceruloplasmin (ferroxidase) 2.49E−14 44.7 22.0 150 209619_PM_at 972 CD74CD74 molecule, major histocompatibility complex, 2.51E−14 1502.3 864.9class II invariant chain 151 208966_PM_x_at 3428 IF116 interferon,gamma-inducible protein 16 2.65E−14 644.9 312.6 152 239237_PM_at — — —2.79E−14 25.3 14.5 153 213566_PM_at 6039 RNASE6 ribonuclease, RNase Afamily, k6 2.82E−14 341.1 134.3 154 201288_PM_at 397 ARHGDIB Rho GDPdissociation inhibitor (GDI) beta 2.86E−14 542.2 308.1 155 209606_PM_at9595 CYTIP cytohesin 1 interacting protein 2.90E−14 79.0 32.9 156205758_PM_at 925 CD8A CD8a molecule 2.91E−14 60.3 22.2 157 202953_PM_at713 C1QB complement component 1, q subcomponent, B chain 3.00E−14 401.1142.5 158 203233_PM_at 3566 IL4R interleukin 4 receptor 3.06E−14 116.772.0 159 205270_PM_s_at 3937 LCP2 lymphocyte cytosolic protein 2 (SH2domain containing 3.12E−14 104.4 44.6 leukocyte protein of 76 kDa) 160223658_PM_at 9424 KCNK6 potassium channel, subfamily K, member 63.18E−14 35.9 22.0 161 202637_PM_s_at 3383 ICAM1 intercellular adhesionmolecule 1 3.18E−14 89.1 45.7 162 202935_PM_s_at 6662 SOX9 SRY (sexdetermining region Y)-box 9 3.18E−14 117.0 46.1 163 217986_PM_s_at 11177BAZ1A bromodomain adjacent to zinc finger domain, 1A 3.21E−14 116.4 62.5164 210915_PM_x_at 28638 TRBC2 T cell receptor beta constant 2 3.27E−14129.7 37.5 165 223343_PM_at 58475 MS4A7 membrane-spanning 4-domains,subfamily A, member 7 3.38E−14 346.0 128.3 166 1552701_PM_a_at 114769CARD16 caspase recruitment domain family, member 16 3.60E−14 273.3 119.8167 226659_PM_at 50619 DEF6 differentially expressed in FDCP 6 homolog(mouse) 3.63E−14 35.2 22.2 168 213502_PM_x_at 91316 LOC91316glucuronidase, beta/immunoglobulin lambda-like 3.63E−14 1214.7 419.3polypeptide 1 pseudogene 169 219332_PM_at 79778 MICALL2 MICAL-like 23.71E−14 68.7 44.4 170 204891_PM_s_at 3932 LCK lymphocyte-specificprotein tyrosine kinase 3.74E−14 43.4 17.8 171 224252_PM_s_at 53827FXYD5 FXYD domain containing ion transport regulators 3.76E−14 73.8 32.5172 242878_PM_at — — — 3.90E−14 53.2 30.1 173 224709_PM_s_at 56990CDC42SE2 CDC42 small effector 2 4.07E−14 1266.2 935.7 174 40420_PM_at6793 STK10 serine/threonine kinase 10 4.32E−14 42.0 24.4 175218084_PM_x_at 53827 FXYD5 FXYD domain containing ion transportregulators 4.52E−14 89.2 39.1 176 218232_PM_at 712 C1QA complementcomponent 1, q subcomponent, A chain 4.63E−14 197.0 85.8 177202208_PM_s_at 10123 ARL4C ADP-ribosylation factor-like 4C 4.63E−14 77.042.2 178 220146_PM_at 51284 TLR7 toll-like receptor 7 4.93E−14 31.6 17.8179 228752_PM_at 84766 EFCAB4B EF-hand calcium binding domain 4B5.05E−14 20.6 12.1 180 208948_PM_s_at 6780 STAU1 staufen, RNA bindingprotein, homolog 1 (Drosophila) 5.23E−14 1766.0 2467.4 181211645_PM_x_at — — — 5.24E−14 166.7 27.4 182 236295_PM_s_at 197358 NLRC3NLR family, CARD domain containing 3 5.28E−14 37.0 18.6 183 224927_PM_at170954 KIAA1949 KIAA1949 5.44E−14 160.2 74.7 184 225258_PM_at 54751FBLIM1 filamin binding LIM protein 1 6.03E−14 228.7 125.4 185202898_PM_at 9672 SDC3 syndecan 3 6.07E−14 64.8 32.0 186 218789_PM_s_at54494 C11orf71 chromosome 11 open reading frame 71 6.12E−14 175.8 280.8187 204912_PM_at 3587 IL1ORA interleukin 10 receptor, alpha 6.25E−14117.2 46.1 188 211582_PM_x_at 7940 LST1 leukocyte specific transcript 16.48E−14 121.2 49.4 189 214617_PM_at 5551 PRF1 perforin 1 (pore formingprotein) 6.77E−14 85.6 40.8 190 231887_PM_s_at 27143 KIAA1274 KIAA12747.00E−14 45.6 30.0 191 223773_PM_s_at 85028 SNHG12 small nucleolar RNAhost gene 12 (non-protein coding) 7.00E−14 174.8 93.2 192 202644_PM_s_at7128 TNFAIP3 tumor necrosis factor, alpha-induced protein 3 7.11E−14278.2 136.6 193 211796_PM_s_at 28638 /// TRBC1 /// T cell receptor betaconstant 1 /// T cell receptor beta 7.13E−14 250.2 63.6 28639 TRBC2constant 2 194 206254_PM_at 1950 EGF epidermal growth factor 7.38E−14176.6 551.3 195 216207_PM_x_at 28299 /// IGKC /// immunoglobulin kappaconstant /// immunoglobulin 7.51E−14 266.3 50.9 28904 /// IGKV1-5 ///kappa variable 1-5 /// immunoglobulin 3514 /// IGKV1D-8 /// 652493 ///LOC652493 652694 /// LOC652694 196 232311_PM_at 567 B2MBeta-2-microglobulin 7.73E−14 83.7 35.2 197 205466_PM_s_at 9957 HS3ST1heparan sulfate (glucosamine) 3-O-sulfotransferase 1 7.84E−14 96.0 42.1198 203332_PM_s_at 3635 INPP5D inositol polyphosphate-5-phosphatase, 145kDa 7.89E−14 42.6 24.0 199 64064_PM_at 55340 GIMAP5 GTPase, IMAP familymember 5 7.98E−14 170.6 111.4 200 211644_PM_x_at 3514 /// IGK@ ///immunoglobulin kappa locus /// immunoglobulin kappa 8.04E−14 246.9 47.550802 IGKC constant

TABLE 10 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier AR vs. TX Fold AR vs TX Changes p-values MAPT −3.09162.99053E−20 GATSL3 /// TBC1D10A −2.11118 3.16645E−17 LOC100288617−2.04457 1.22069E−16 ACSM2A /// ACSM2B −1.95127 1.57754E−16 AKR7A3−2.05229 1.75126E−16 FTCD −2.71817 4.97004E−16 GPHN −1.81578 1.17623E−15SLC13A2 −2.18768 3.58849E−15 DNAJC6 −1.76929 5.20867E−15 SLC17A3−1.84587  6.8169E−15 RGN −2.37098 7.84126E−15 RDH12 −4.26697 9.20572E−15CRYAA −2.74677 1.32872E−14 SLC7A13 −4.66793 1.86514E−14 ALB −6.827251.89192E−14 HAGH −1.81366 7.33148E−14 ALDH6A1 −2.20224 8.91056E−14 HBA1/// HBA2 −1.88978 9.34894E−14 CLYBL −1.8789 9.81129E−14 FLJ42875−1.97495 1.45096E−13 FBP1 −2.28393 2.00607E−13 FABP1 −2.861642.18592E−13 SLC2A5 −2.17033 2.79078E−13 ETNK2 −1.92877  3.8779E−13 ADH6−2.40152  4.1841E−13 PEPD −1.97852 4.63261E−13 PPAPDC1A −2.027996.60672E−13 CLCNKA /// CLCNKB −1.89433  7.0432E−13 LPPR1 −2.466088.09001E−13 PNPLA3 −2.89596  8.8556E−13 GSTA1 −1.92437 1.16843E−12 DHDH−3.08428 1.29848E−12 HPD −3.98241 1.30435E−12 HIBADH −1.89283 1.4274E−12 EPHX2 −2.11633 1.98928E−12 MAF −1.76205  2.0964E−12 TMEM178−1.78847 2.20622E−12 LOC642891 −1.73984 2.80875E−12 LOC285733 −1.959943.23545E−12 FMO1 −2.25513 4.50736E−12 GGT6 −2.66101  4.6681E−12 FLJ22763−1.93955 5.14404E−12 SLC6A13 −2.24946 5.34043E−12 PTGER3 −1.811125.52121E−12 SLC30A8 −1.84747 6.11346E−12 IGSF11 −2.21766 6.61316E−12CYP3A7 −2.91581 6.87921E−12 LRRC43 −1.9031 8.14997E−12 APOH −5.574538.27826E−12 FAM83D −2.32876 8.74785E−12 ASPA −2.31579 8.80911E−12 PROZ−2.78599 9.23614E−12 TNNC1 −1.79223 1.01194E−11 PRODH2 −3.386741.25579E−11 C15orf59 −1.77893 1.64304E−11 GLYCTK −1.82955 1.70772E−11KMO −2.36048  1.9935E−11 KNG1 −2.63283 2.20875E−11 LDHD −2.571912.27083E−11 CDHR5 −2.63356  2.2819E−11 HAAO −1.84903 2.42843E−11 ACSF2−2.00048 2.48828E−11 SLC37A4 −2.01845 2.87337E−11 TINAG −1.85442.91111E−11 PBLD −1.91883  3.1387E−11 CYP46A1 −1.93139 3.14729E−11 CNDP1−2.3083 3.26491E−11 SLC12A6 −2.31688 3.56291E−11 AFM −6.660983.67152E−11 C10orf72 −1.76232 3.70569E−11 CLCNKB −1.96378 3.83317E−11SLC6A19 −3.99223  3.8873E−11 ATP6V0D2 −2.33798 4.04264E−11 RHBG −1.808074.19595E−11 PM20D1 −2.51866 4.26226E−11 ACADSB −2.10959 4.46915E−11CLDN8 −2.16632 4.88256E−11 DEPDC7 −1.85669 5.00337E−11 C2orf54 −1.79555.39601E−11 FAM151A −5.32405 5.61188E−11 SLC22A11 −2.10205 5.65313E−11RALYL −2.47687 5.78764E−11 ACSM5 −1.88278 5.80564E−11 TMEM207 −2.952056.13829E−11 RUNDC3B −2.06687 6.42529E−11 ANGPTL3 −2.56539 7.90237E−11LOC388588 −2.00381 9.24089E−11 APOM −2.50604 9.25689E−11 KCNJ15 −2.035619.83258E−11 ZYG11A −2.40225 1.04754E−10 AGXT −2.6006 1.17202E−10 UPB1−2.7529 1.20683E−10 CAPN3 −2.1059 1.23158E−10 ENTPD5 −1.955491.28898E−10 METTL7B −2.18054  1.2938E−10 KHK −2.60696 1.31642E−10LOC284578 −1.90116 1.38269E−10 PDZD3 −1.83091 1.46276E−10 SERPINA5−1.74937  1.6048E−10 TM4SF5 −2.64814 1.71797E−10 NT5E −1.778761.82759E−10 CTH −1.95197 1.84254E−10 AQP11 −2.0271 2.48812E−10 SLC34A3−2.04588 2.84241E−10 DMGDH −2.55618 3.18184E−10 SORD −2.244763.30972E−10 DPEP1 −3.05594 3.61209E−10 LOC153328 −1.8068 3.80205E−10FOLH1 −2.32575 3.80509E−10 PLCXD3 −1.76665 3.89412E−10 OSTBETA −2.436013.93207E−10 ABCC2 −2.53512 3.97325E−10 ECHDC3 −1.73755 3.97793E−10 CRYL1−1.82953 4.00197E−10 SLC13A3 −2.13984 4.04239E−10 SLC16A10 −2.44322 4.7162E−10 C9orf66 −3.0274 5.03177E−10 GPR98 −4.26595  6.258E−10 HAO2−3.23686 6.39362E−10 CLPTM1 −2.00927 6.66131E−10 ACP5 −2.790786.66908E−10 ISOC2 −1.8934 6.90662E−10 FOLH1B −2.76374 7.11947E−10 TLN2−2.37363 7.32503E−10 GALM −1.91896 7.36792E−10 OSTalpha −2.780667.52226E−10 SLC5A11 −2.75896 8.19688E−10 EPHX1 −2.00145 8.56848E−10 GAS2−1.80474 9.28707E−10 NOX4 −2.05807 1.01236E−09 FGF1 −1.82212 1.24912E−09FCAMR −2.07507 1.27892E−09 LOC100130691 −2.59795 1.29088E−09 ARSE−1.80351 1.31681E−09 RETSAT −1.76392 1.36913E−09 SLC22A7 −2.264761.51477E−09 C7orf10 −2.15451  1.6297E−09 SERPINA6 −3.41598 1.63356E−09CPNE6 −1.8087 1.66697E−09 MAP7D2 −1.74996 1.92036E−09 SEPP1 −1.901672.17198E−09 AGXT2L1 −3.23817 2.32327E−09 C4orf12 −2.14021  2.3661E−09RAB11FIP3 −2.44555 2.48943E−09 GK /// GK3P −1.83394 2.70788E−09 PC−2.6032 2.83833E−09 PANK1 −2.15352 3.13166E−09 NAGS −2.18509 3.13645E−09MME −2.67889 3.20928E−09 SUSD2 −2.3654 3.35377E−09 SLC3A2 −2.003453.38775E−09 AMDHD1 −2.76004 3.69681E−09 MTTP −2.43293 3.87705E−09 PHYH−1.84295 3.99573E−09 CYP17A1 −2.01122 4.11531E−09 BPHL −2.375994.15467E−09 ASB9 −2.91835 4.72846E−09 CYP2B6 /// CYP2B7P1 −3.27876 4.9278E−09 HADH −1.95557 5.18111E−09 AQP7 −2.04383 5.46752E−09 SLC10A2−2.73836  5.7561E−09 ZMYND12 −2.1429 5.83754E−09 HIBCH −1.804355.87555E−09 CYP4F3 −2.53506 5.96184E−09 MGAM −2.1997 6.22815E−09 CYP4F2/// CYP4F3 −3.3825 6.27466E−09 SLC22A12 −2.11852 7.58307E−09 QPRT−1.87851 7.79844E−09 THY1 −2.21775 7.97833E−09 SNTA1 −1.9336 9.67285E−09ACOT1 /// ACOT2 −1.78472 9.85738E−09 SLC2A2 −1.89882 1.00173E−08 MYH8−2.11404 1.02047E−08 SLC6A18 −1.86164 1.08202E−08 PRAP1 −2.213581.09557E−08 MRO −4.28065 1.13633E−08 EHHADH −1.77338 1.14067E−08 DIO1−3.00979 1.33367E−08 TTC36 −2.36726 1.36035E−08 PSAT1 −2.38335 1.3782E−08 ATP6V1G3 −2.23634 1.38236E−08 ACE2 −1.79037 1.42874E−08 GJB1−1.96896 1.44201E−08 SLC22A6 −2.86071 1.46506E−08 TRIM50 −2.723591.48482E−08 SOST −3.04185 1.52557E−08 ESPL1 −2.15374  1.5528E−08 CALB1−2.65581 1.57872E−08 AZGP1 −2.47351 1.60913E−08 PXMP2 −1.956471.66904E−08 TMEM120A −1.83373 1.84556E−08 KLK1 −4.86671 1.85761E−08SLC22A8 −3.51523 1.87712E−08 FUT3 −1.8837 1.93982E−08 SLC7A8 −1.916591.99023E−08 PDK2 −1.97685 2.13903E−08 ANXA9 −2.33358 2.23676E−08 P4HA2−1.94378  2.2935E−08 SLC34A1 −2.68387 2.29418E−08 ACY3 −2.026462.33713E−08 C2orf40 −2.555 2.37292E−08 FUT6 −1.92525 2.41426E−08 ACOT7−1.95494 2.45886E−08 PNPLA1 −1.78439 2.48072E−08 ABAT −1.829212.80329E−08 UPP2 −3.15846 3.23662E−08 MOSC2 −2.32105 3.32422E−08LOC389332 −1.76102 3.35953E−08 ALDH1B1 −1.77365  3.4294E−08 GPC5−2.35239 3.47079E−08 AMN −2.01448 3.76136E−08 SLC22A13 −1.951663.79569E−08 CALML3 −2.53824  3.9564E−08 PDXP /// SH3BP1 −1.904473.96765E−08 RHCG −2.56004 4.20488E−08 VEPH1 −1.98345 4.37645E−08 GC−1.79578  4.8255E−08 RNF186 −2.27866 5.26723E−08 GAL3ST1 −1.970995.32145E−08 GLYAT −2.45554 5.56573E−08 BPI −1.8412 5.56594E−08 REEP6−2.30623 5.70126E−08 ACOT4 −1.9105 6.45152E−08 MUC13 −2.122456.98563E−08 NPR3 −2.54362 7.01278E−08 RAB11FIP5 −1.7414 7.17374E−08SLC12A3 −2.79439 7.49512E−08 EGF −3.16114 8.28225E−08 HMGCS2 −2.18718.66021E−08 SLC5A10 −2.39748 8.98765E−08 TUBAL3 −2.16082 9.26757E−08LOC145837 −2.93781 9.38032E−08 SLC7A9 −2.43122  9.7596E−08 LOC727944−2.81085 9.79329E−08 CYP4F2 −3.50762 1.00843E−07 SLC5A2 −1.910241.07955E−07 DHDPSL −1.82011 1.08169E−07 MIOX −3.2511 1.10411E−07 CPN2−2.12523  1.1914E−07 SLC7A7 −2.02602 1.22723E−07 CYP4A11 /// CYP4A22−1.98138 1.28529E−07 G6PC −3.25106 1.28591E−07 DDC −2.62111 1.28799E−07RGS7 −1.76457 1.30087E−07 PRNP −1.77477 1.35141E−07 SLC4A9 −1.95261 1.3642E−07 PAH −3.20056 1.43122E−07 ESRRG −1.73711 1.52263E−07 SLC26A9−1.74242 1.53483E−07 LOC644242 −1.91415 1.67473E−07 CTXN3 −4.39211.68953E−07 UGT1A8 /// UGT1A9 −3.14494 1.86107E−07 PCK2 −2.702461.87667E−07 ABP1 −2.82479 2.01634E−07 HNF4G −1.92184 2.02132E−07 CYP3A4−2.29038 2.02506E−07 C21orf33 −1.76567  2.1446E−07 CUBN −2.302272.21237E−07 FMO4 −2.66707 2.24563E−07 GPD1 −2.0302 2.35214E−07 APOC3−2.11763 2.36005E−07 GK3P −2.07563 2.40413E−07 GK −2.20019 2.40894E−07ENPP6 −2.67481 2.45316E−07 ASS1 −2.1157 2.62889E−07 DAO −3.444542.65842E−07 HPGD −2.38564 2.72233E−07 GCHFR −1.75487 2.94943E−07 TMEM174−3.55663 2.96489E−07 CHDH −2.24862 2.99682E−07 CLEC18A /// CLEC18B ///CLEC18C −2.16625 3.19031E−07 UMOD −2.26429 3.21104E−07 HRG −2.987613.27498E−07 TSKU −1.73688 3.29833E−07 HSD17B14 −2.18571 3.50438E−07SLC23A3 −2.59008 3.53828E−07 ITLN1 −1.87748 3.67115E−07 CAMK2G −1.736224.07919E−07 GLYATL1 −2.38225 4.39935E−07 UGT2B28 −1.99594 5.11969E−07A2LD1 −2.47322 5.71176E−07 ALAD −1.88239 5.71331E−07 HSD11B2 −2.185975.81499E−07 GDPD3 −2.23762 6.32071E−07 CSDC2 −1.98441 6.72915E−07 AQP3−1.94567 7.02888E−07 PLG −3.86142 7.96637E−07 PGPEP1 −1.894388.15391E−07 LPA /// PLG −4.61775 8.82027E−07 PAQR7 −1.85889 9.01772E−07SLC27A2 −1.92807 9.02555E−07 C18orf56 −2.04976 9.15996E−07 SLC23A1−2.80195 9.52247E−07 ZDHHC9 −1.73908 1.08396E−06 C12orf64 −2.196741.10586E−06 SLC25A10 −2.41468 1.18238E−06 ANK2 −1.90934 1.20132E−06 USP2−2.02703 1.21673E−06 COLEC11 −2.43455 1.36731E−06 PKLR −2.178751.37898E−06 GPT2 −2.34268 1.46992E−06 CDHR2 −1.91669  1.4866E−06 ACOX2−2.2757 1.49911E−06 XPNPEP2 −2.99801 1.50402E−06 HEPACAM2 −2.29251.60973E−06 UBE2QL1 −2.15896 1.63642E−06 SLC39A5 −2.74063 1.69299E−06TMEM106A −2.02395 1.69787E−06 ATP6V0E2 −1.78673 1.86877E−06 AK3L1−1.93512 1.90809E−06 AP1M2 −1.9461 2.03528E−06 ARSF −2.25573 2.07251E−06AGXT2 −2.22933 2.16372E−06 DAK −2.20229 2.36754E−06 SLC2A12 −1.770172.43975E−06 ALDOB −1.76925 2.55745E−06 ALDH4A1 −2.03136 2.70075E−06SFXN2 −2.60877 3.45507E−06 DIP2C −2.05583 3.64214E−06 CTSL2 −2.529683.71723E−06 SLC28A2 −1.93371 3.82506E−06 FUZ −1.79456 3.95111E−06C19orf69 −2.09232 4.14502E−06 REN −1.85222 5.09502E−06 C12orf59 −1.750895.13955E−06 TMEM132E −2.18984 5.33005E−06 ZGPAT −2.44576 5.34764E−06PLEKHA5 −1.96129 5.89183E−06 GRAMD1C −1.74771 6.11458E−06 DUSP9 −1.915896.38564E−06 SLC22A4 −1.89514 6.66544E−06 SORCS1 −1.85441 7.11084E−06STC2 −1.81501 7.21338E−06 LEAP2 −2.38133 7.40637E−06 KCNN2 −2.250187.51697E−06 IDH1 −1.8165 7.62782E−06 NAT8B −2.42914  7.8931E−06 SLC5A9−1.75921 9.82367E−06 TM7SF2 −1.92027  9.949E−06 PLA2G12B −2.139011.09806E−05 CLRN3 −1.91704 1.10718E−05 PCYT2 −1.80892 1.18832E−05 SLC4A1−2.48783 1.19999E−05 GSTA3 −2.10371 1.22206E−05 PVALB −2.29588 1.2453E−05 ATP6V1C2 −1.98979 1.29667E−05 PIK3C2G −1.76675 1.29855E−05DHRS11 −1.74961 1.35061E−05 NEDD4L −1.89373 1.41229E−05 AQP2 −2.03409 1.4403E−05 SLC5A12 −2.19859 1.46257E−05 AS3MT −1.83726 1.71006E−05 AHCY−1.8639 1.78963E−05 PIPOX −2.48991 1.79528E−05 CEL −1.82327  1.812E−05DCXR −2.16471 1.92539E−05 CYP4A11 −1.86722 2.10549E−05 C4orf31 −1.828112.58079E−05 CYP4A22 −2.07108 2.87185E−05 GLTPD2 −2.11301 3.06781E−05MAP7 −2.19683 3.85037E−05 TMEM86A −1.89253 4.99658E−05 SLC25A1 −1.82031 5.0373E−05 DPP4 −2.22087 5.57627E−05 IYD −2.08101  5.9404E−05 UGT1A1/// UGT1A10 /// UGT1A3 /// −1.8015 6.24555E−05 UGT1A4 /// UGT1A5 ///UGT1A6 /// UGT1A7 /// UGT1A8 /// UGT1A9 A1CF −2.20926 6.47761E−05C5orf23 −1.93642 7.84432E−05 NECAB2 −3.13495 8.16616E−05 RNLS −1.980848.33272E−05 DNMT3L −2.65364 8.93726E−05 NPHS2 −1.90039 9.36204E−05 RAB17−1.79062 0.000102017 ABCC6 /// LOC100292715 −1.9274 0.000110765 MUC15−1.84539 0.000111654 PPP1R16A −1.89951 0.000117374 KCNK5 −1.766870.000126376 LOC100287428 −1.84499 0.000134038 APOE −1.85436 0.000138448GIPC2 −1.94676 0.00014941 C11orf54 −1.75689 0.000156408 SLC17A5 −1.742230.000163743 KL −1.8958 0.000167359 CYP8B1 −2.25808 0.000167576 DPYS−2.08317 0.000176199 SH3GL2 −1.91695 0.000186793 DHRS4 /// DHRS4L2−2.07083 0.000208985 ALLC −1.78998 0.000234174 PTGR1 −1.750220.000261226 CRHBP −2.11391 0.00027295 C9orf103 −1.88471 0.00056122SLC28A1 −1.77452 0.001183781 SLC16A9 −1.89967 0.001212523 ABHD6 −1.80110.001402915 BHMT −1.86117 0.003092475 CRYM −1.84582 0.004771269 RENBP−1.85157 0.00698823 ABCC6P1 −1.87408 0.008304558 L2HGDH −1.795730.025300732 ACSM3 −1.79192 0.029160166

TABLE 11 Biopsy Expression Profiling of Kidney Transplants: 2-WayClassifier CAN/IFTA vs. TX Fold IFTA vs TX Changes p-values TMSB101.51781 3.98249E−09 S100A11 1.56822 1.38989E−08 SLCO2A1 1.639663.44566E−08 HLA-DRB1 /// HLA-DRB4 1.58546  4.3499E−08 HLA-DRB1 1.552715.38233E−08 SLC2A12 −1.70211 5.46662E−08 VCAM1 1.65026 6.48536E−08 UPP11.63064 1.16852E−07 LDLRAD3 1.65949 1.26498E−07 SH2B3 1.545931.36597E−07 EGF −2.89482 1.71342E−07 MLL3 1.65249 2.14515E−07 TRIM50−2.12508 2.24723E−07 AKNA 1.53574 2.41689E−07 GIMAP5 1.56499 2.67192E−07HLA-DMB 1.57184 2.90879E−07 CKLF 1.55882 4.12568E−07 SLC34A3 −1.630665.51514E−07 HS3ST1 1.53641 5.56865E−07 MARCKSL1 1.61621 5.84424E−07SLC16A7 −1.62101 7.39903E−07 B2M 1.50123 9.50899E−07 TSPAN13 1.53438 9.8449E−07 PARVG 1.58576 1.07603E−06 TUBA1A 1.62753 1.08009E−06 GUCY1A31.50036 1.08029E−06 HLA-F 1.52472  1.176E−06 PARP12 1.51868 1.20425E−06GAB3 1.51079  1.3961E−06 ELF4 1.63538 1.52125E−06 FAM83D −1.892171.53199E−06 PARP14 1.56897 1.53554E−06 LCP2 1.6147 1.54053E−06 SAMD9L1.61164 1.55377E−06 ASNS 1.64299 1.56911E−06 RNF213 1.52854 1.57479E−06FERMT3 1.57985 1.59445E−06 APOL1 1.57279 1.60354E−06 TNFAIP8L2 1.515021.70532E−06 PSMB10 1.54422 1.75542E−06 PMEPA1 1.66003 2.04838E−06 RAB311.6387 2.08849E−06 PRR24 1.54645 2.08952E−06 SMPDL3A −1.508692.10279E−06 RRAS 1.51587 2.26269E−06 PVALB −2.2572 2.72451E−06 PSMB81.56268 2.72636E−06 TM4SF5 −1.80583 2.77734E−06 A2LD1 −1.771362.85983E−06 NUAK1 1.53844 2.92207E−06 MCAM 1.6564 3.11631E−06 RNLS−1.5429 3.14475E−06 ASB9 −1.79403  3.1805E−06 IL15RA 1.60325 3.26002E−06LOC645895 /// MYBL1 −1.52279 3.36189E−06 MST4 1.57651 3.39448E−06 COL4A21.61415 3.54382E−06 NPHS1 −1.77154 3.58126E−06 KCNN2 −1.951693.61421E−06 NECAB2 −1.83644 3.61683E−06 GGT5 1.5766 4.21195E−06 EFCAB4B1.52589 4.27242E−06 TLR2 1.58142 4.27452E−06 C21orf63 1.535374.30722E−06 GLTPD2 −1.71616 5.05996E−06 TMPRSS2 −1.52104 5.44787E−06 MRO−2.43891  5.4527E−06 MBOAT1 1.54611 5.70031E−06 KLK1 −2.708 6.20862E−06IDH1 −1.50367 6.55865E−06 ARRDC2 1.66068 6.57954E−06 UBE2QL1 −1.768956.76469E−06 ALB −4.11767 6.82617E−06 ZYG11A −1.92637 6.83448E−06 SAMD31.58295 7.06467E−06 GPR98 −2.62258 7.19656E−06 MLKL 1.56773 7.21744E−06SLC2A5 −1.91328 7.25168E−06 KLF4 1.56399 7.33393E−06 ITPRIPL2 1.556437.34185E−06 APOL6 1.55686 7.34937E−06 LOC153328 −1.54066 7.35108E−06SPATA17 −1.58696 7.36371E−06 IRF7 1.57063 7.78387E−06 CYP3A4 −1.684778.04902E−06 CIITA 1.59697 8.05175E−06 PRKCDBP 1.52369 8.25806E−06 CTSL2−2.2103 9.30206E−06 C12orf35 1.61211 9.52663E−06 GATSL3 /// TBC1D10A−1.59015 1.02924E−05 CCR5 1.61292 1.04302E−05 LOC91316 1.553321.09782E−05 LHFP 1.61605 1.10787E−05 ITGAL 1.65885 1.22092E−05 SLC12A3−2.04892 1.22155E−05 PLAUR 1.60949 1.23043E−05 PPAP2C 1.635071.23102E−05 IER5 1.52273 1.23557E−05 CYP3A7 −2.0504 1.28185E−05 SLC4A1−1.81494 1.34578E−05 EBF1 1.52946 1.35965E−05 IL16 1.51006 1.50211E−05HRG −1.90476 1.51876E−05 PRKCB 1.59664 1.56904E−05 SYCE1L 1.619791.58566E−05 PARP8 1.63188 1.59697E−05 FAM65B 1.56435 1.69584E−05 PTGER3−1.89681  1.7065E−05 FIBIN 1.62824 1.74203E−05 RNF182 1.508411.82387E−05 ANGPTL3 −2.04101 1.82518E−05 PHACTR3 1.51835 1.84497E−05FKBP11 1.64574 1.84525E−05 CARD6 1.50388 1.84747E−05 CD1D 1.596021.85397E−05 GMFG 1.64971 1.85486E−05 AFM −3.09543 1.90192E−05 LAYN1.50713 1.91771E−05 BIN2 1.5738 2.00422E−05 HADH −1.53494 2.01328E−05CLDN3 1.58139 2.01595E−05 IL8 1.6304 2.06309E−05 CHST15 1.570682.08254E−05 HLA-DOB 1.57448 2.08412E−05 ALDH3A2 −1.51338 2.24457E−05G6PC −2.06203 2.31973E−05 LOC153684 1.51481 2.35676E−05 BTN3A3 1.583852.36092E−05 LOC96610 1.54288 2.37697E−05 PRSS2 /// PRSS3 −1.556842.39309E−05 AADAT −1.50725 2.45144E−05 ATP6V1C2 −1.87663 2.51529E−05FAM84A 1.63054 2.72254E−05 MYADM 1.57406 2.72605E−05 LOC727944 −2.18982.74724E−05 MOSC2 −1.62916 2.95301E−05 SLCO3A1 1.55284 2.95888E−05 RSPH11.64647 2.97493E−05 C1orf192 1.60412 2.99924E−05 NNT −1.646473.17285E−05 SEL1L3 1.61562 3.17683E−05 C9orf103 −1.61381 3.20544E−05CD74 1.62637 3.27408E−05 JUN 1.63578 3.27609E−05 RGS7 −1.539763.27856E−05 AOAH 1.58066 3.28623E−05 FAM24B −1.53027 3.30462E−05 NCKAP1L1.59544  3.3182E−05 DCDC2 1.52668 3.46016E−05 PLCL1 −1.5323 3.47883E−05SERPINA6 −1.72919  3.5546E−05 CPNE6 −1.52074 3.67948E−05 LPCAT1 1.629073.68576E−05 ADM −1.53891 3.74019E−05 ELMOD1 −1.64162 3.79781E−05 MSL11.56313 3.84376E−05 PLA2G12B −1.7228 3.89562E−05 TUBB6 1.610074.06407E−05 CDC42EP5 1.61246 4.08613E−05 TLN2 −1.51459 4.21746E−05 TAP21.59627 4.28878E−05 EMP3 1.52713 4.48564E−05 VEGFA −1.50122  4.7167E−05SIRPG 1.57626 5.08523E−05 HCK 1.62706 5.08921E−05 ALAD −1.517525.34836E−05 GK3P −1.67708 5.42274E−05 CCR2 1.5253 5.72632E−05 GSTA3−1.5681 5.78607E−05 FYB 1.55028 5.88739E−05 ACADSB −1.50559 6.10124E−05FOLH1B −1.9831 6.11789E−05 CALML3 −1.94826 6.21615E−05 LOC100289727 ///NCF1 /// NCF1B /// 1.58313 6.39151E−05 NCF1C PIGR 1.50262  6.4456E−05SLC16A10 −1.85159 6.50594E−05 ANXA3 1.66158 6.59979E−05 GK −1.882066.68014E−05 MX1 1.59966 6.80462E−05 SORCS1 −1.61133 6.84525E−05 DNMT3L−1.9539 7.10165E−05 ARHGAP28 −1.56725 7.16985E−05 APOH −2.434137.25988E−05 RHCG −1.86366 7.39257E−05 C15orf59 −1.54017 7.44282E−05 MX21.65683 7.78195E−05 IFI27 1.51629 7.82297E−05 FOLH1 −1.96392 8.12122E−05TMEM207 −2.29284 8.39494E−05 GPR65 1.63506 9.08757E−05 CAMK1D ///LOC283070 1.52061 9.22035E−05 HDAC9 1.56346 9.31902E−05 WT1 −1.529699.37583E−05 CMPK2 1.60157 9.46544E−05 SERPINH1 1.65676 9.67787E−05 CAPN61.62011 0.00010357 FLNA 1.50644 0.00010568 PRSS3 −1.52475 0.000108476DKK3 1.51087 0.000117202 RUNX3 1.54606 0.000128211 LOC100132891 1.59010.000128519 AQP11 −1.57835 0.00013752 LOC388588 −1.5056 0.000138318HS6ST2 −1.80972 0.000140022 GABBR1 1.58018 0.000140037 CRTAM 1.521250.000140069 SUSD2 −1.51355 0.000142327 DPP4 −1.67197 0.000145216 SFXN2−1.6002 0.000145357 C12orf64 −2.01929 0.000155923 PROZ −1.809740.000157604 FCGR3B 1.55481 0.00015864 ACSL4 1.50161 0.000161869 DUSP51.54175 0.000163447 GPR18 1.60959 0.000166523 ESPL1 −1.7324 0.000171456ATP6V1G3 −1.77982 0.000172096 DNAJC3 1.58767 0.00017247 S100A9 1.547460.000178741 CD27 1.64479 0.000180879 MUC1 1.63077 0.000186774 GALNT31.51347 0.000188942 MTHFD2 1.57956 0.00019287 TTC36 −1.52589 0.000195258P2RY8 1.53511 0.000195337 EPB41L3 −1.52238 0.00020416 ANK2 −1.647840.000204429 LOC100131781 −2.9471 0.000204454 ABCC2 −1.68245 0.000207988HEPACAM2 −1.84159 0.000209517 NTM 1.51173 0.000212033 KLRC1 /// KLRC21.63195 0.000220071 KRT19 1.55043 0.000225436 KYNU −1.8178 0.000228908VEPH1 −1.62722 0.000229217 SPTLC1 −1.64623 0.000231038 TARP 1.622280.000243027 ARSF −1.50712 0.000250355 PNPLA3 −1.62818 0.000251061 AMDHD1−1.6455 0.000254162 TBC1D10C 1.5601 0.000256812 SELP 1.52502 0.000258767RCSD1 1.57125 0.000261223 TMEM178 −1.58308 0.000263195 SLC6A19 −1.822720.000275501 P2RY14 1.59513 0.000279279 APOC3 −1.53424 0.000300535 ACOX2−1.62014 0.000301637 FHL2 1.52656 0.000311563 ALOX5AP 1.607410.000314792 C15orf48 1.65767 0.000314854 C10orf128 1.59052 0.000317657PRELP 1.60495 0.000320644 CELF2 1.52413 0.000324376 ATP6V0D2 −1.754110.000325699 MAP7 −1.79697 0.000334732 SLC23A3 −1.54687 0.000334785 NPR3−1.79863 0.000368992 CRISPLD2 1.53183 0.000389441 RALYL −1.745430.00039422 CTH −1.55662 0.000396559 SLC4A9 −1.52135 0.000411691 GEM1.54158 0.000427531 SELE 1.57885 0.00043229 WNT5A 1.52321 0.000435362ARPC3 1.66106 0.000439626 CYBB 1.51692 0.00045065 RGS1 1.566990.000457779 PRNP −2.36306 0.000488807 IGSF11 −1.63702 0.000509764 GK ///GK3P −1.55526 0.000515407 LOC1518 −1.51858 0.000515767 SLC5A11 −1.609130.000521049 RMND1 −1.55838 0.000540732 HPGD −1.78558 0.00054091 DHDH−1.71486 0.000544292 NAT8B −1.55811 0.000549575 SAR1B −1.640730.000558645 C4orf31 −1.85662 0.000571234 RDH12 −2.01518 0.000572799SAMSN1 1.60145 0.000591216 C1S 1.50415 0.000607667 FLJ42875 −1.605470.000608022 CCDC3 1.57889 0.000623944 DDN −1.50715 0.000626257 GNB5−1.55044 0.000634195 SOST −2.11137 0.000644151 LPPR1 −1.822530.000649757 CFH 1.6367 0.000669139 MME −1.64596 0.000674042 LOC1002872371.56531 0.000764834 SRGN 1.54084 0.00076549 KNG1 −1.62639 0.000769626AGXT2L1 −2.02162 0.00077628 GBP1 1.60205 0.000778436 SPON1 1.562250.000788929 CFH /// CFHR1 1.52603 0.000790894 LY75 1.61206 0.000791747HIBADH −1.79186 0.000811983 MAPT −1.60132 0.000852554 TACSTD2 1.520970.000874577 PTTG1 1.51809 0.00091356 BASP1 1.54985 0.000923589 GPRC5A1.52408 0.000927775 PRKX /// PRKY 1.54463 0.000970822 MTTP −1.695930.000995856 TNKS2 1.51761 0.001002234 NR1D2 1.55627 0.001020233 PRODH2−1.74304 0.001020309 FAM134C 1.5638 0.001050607 C1orf186 1.601480.001110359 MYL9 1.55261 0.001139867 TPD52 −1.70752 0.001169747 FAM151A−1.97601 0.001205059 ZGPAT −1.57946 0.001246363 ACOT7 −1.528 0.001258945FH −1.68361 0.00125963 SORD −1.56419 0.001267712 PAH −1.902540.001272156 MUC13 −1.69478 0.001290676 NUDT6 −1.5431 0.001345321 SLC28A2−1.52768 0.001385666 OSTalpha −1.60696 0.001399651 TMEM174 −1.657130.001454055 TNC 1.60863 0.001486039 PBLD −1.55582 0.001556271 AGXT−1.75973 0.001590847 DDAH1 −1.6755 0.001592385 CHI3L1 −1.653850.001602868 FOS 1.59586 0.001625845 BAX 1.60078 0.001631275 CES1 1.630450.001669311 TYRP1 −1.82362 0.001697884 SLC10A2 −1.73028 0.001716763 TDO21.63563 0.001726221 NEURL1B 1.56331 0.001726312 CTXN3 −1.792820.001743693 DMGDH −1.51241 0.001749636 NFAT5 1.59118 0.001753736 TUBAL3−1.51042 0.001804062 IGFBP6 1.58017 0.001901916 DIO1 −1.598480.001910455 SLC23A1 −1.52549 0.001946197 THY1 −1.53788 0.001996268 PSAT1−1.69764 0.002002125 DAO −1.60066 0.002066006 PM20D1 −1.711270.002086353 CCL3 /// CCL3L1 /// CCL3L3 1.51353 0.002087392 KMO −1.603430.002283512 IFI44L 1.64407 0.0023117 CRYAA −1.80094 0.002325131 OSTBETA−1.58545 0.002365191 LOC100288332 /// 1.62547 0.002388231 LOC100288583/// NPIPL3 PLG −1.78244 0.002461498 LPL −1.66864 0.00246504 CCR7 1.511680.002557407 LRRC28 −1.56336 0.002620124 RRAD 1.53482 0.002741312 MUC15−1.59237 0.002741823 IFI6 1.61934 0.002829231 DDC −1.7423 0.002861605CRHBP −1.99859 0.002945064 CYP2B6 /// CYP2B7P1 −1.75265 0.002949884DNAJC12 −1.64575 0.00299662 HIRA 1.51289 0.003063139 CCL20 1.606580.003165424 LOC145837 −1.7347 0.003171816 FGF1 −1.64515 0.003348491ARHGDIB 1.51724 0.003359731 GPHN −1.54106 0.003390414 C9orf66 −1.515690.003831676 PTPRO −1.52077 0.004034566 UPP2 −1.74057 0.00418584 GSN−1.6508 0.00438635 SLC34A1 −1.53144 0.004434079 PCOLCE2 −1.586430.004936761 NELL2 1.50929 0.005435169 IFT57 −1.60347 0.005534224 ASPN−1.54129 0.005627179 GPC5 −1.62746 0.006502554 TAC1 1.62375 0.00651702VAMP3 −1.58292 0.006552971 LPA /// PLG −1.76434 0.006796348 ADAMDEC11.50784 0.006940972 CXCL13 1.55288 0.007006212 GPR34 1.58029 0.007177428SLC7A13 −2.2753 0.007781262 NCRNA00182 1.65305 0.009416748 CNN1 1.50220.009933538 SRPX 1.60599 0.010174115 ITLN1 −1.80647 0.01035312 CYP4F2/// CYP4F3 −1.58922 0.01326078 UGT1A8 /// UGT1A9 −1.69177 0.018037058CMYA5 −1.51105 0.020198567 OGN −1.60803 0.022700207 PDGFRL −1.539180.02458871 LEFTY1 1.66065 0.031306922 MYH8 −1.53175 0.038086016 TNNC1−1.86111 0.042271383 MFAP5 −1.61249 0.043993201

We claim:
 1. A method of prognosing, detecting, diagnosing or monitoringa kidney transplant rejection or injury, or lack thereof in a subject,comprising: (a) obtaining nucleic acids of interest, wherein the nucleicacids of interest comprise mRNA extracted from a sample from a subjector nucleic acids derived from the mRNA extracted from the sample fromthe subject; (b) detecting expression levels in the subject of the atleast five genes selected from the at least one of Tables 7, 8, 9, 10,and 11, using the nucleic acids of interest obtained in step (a); and(c) prognosing, detecting, diagnosing or monitoring the kidneytransplant rejection or injury, or lack thereof in the subject from theexpression levels detected in step (c).
 2. The method of claim 1,further comprising contacting the nucleic acids of interest with probes,wherein the probes are specific for the at least five genes selected instep (b).
 3. The method of claim 1, wherein the sample from the subjectis a biopsy sample.
 4. The method of claim 1, wherein the subject hasacute rejection (AR), acute dysfunction no rejection (ADNR), chronicallograft nephropathy (CAN), or well-functioning normal transplant (TX).5. The method of claim 1, wherein for each of the at least five genes,step (c) comprises comparing the expression level of the gene in thesubject to one or more reference expression levels of the geneassociated with AR, ADNR, CAN, or TX.
 6. The method of claim 5, whereinstep (c) further comprises for each of the at least five genes assigningthe expression level of the gene in the subject a value or otherdesignation providing an indication whether the subject has AR, ADNR,CAN, or TX.
 7. The method of claim 6, wherein the expression level ofeach of the at least five genes is assigned a value on a normalizedscale of values associated with a range of expression levels in kidneytransplant patients with AR, ADNR, CAN, or TX.
 8. The method of claim 7,wherein the expression level of each of the at least five genes isassigned a value or other designation providing an indication that thesubject has or is at risk of AR, ADNR, CAN, has well-functioning normaltransplant, or that the expression level is uninformative.
 9. The methodof claim 6, wherein step (c) further comprises combining the values ordesignations for each of the genes to provide a combined value ordesignation providing an indication whether the subject has or is atrisk of AR, ADNR, CAN, or has TX.
 10. The method of claim 9, wherein themethod is repeated at different times on the subject.
 11. The method ofclaim 9, wherein the subject is receiving a drug, and a change in thecombined value or designation over time provides an indication of theeffectiveness of the drug.
 12. The method of claim 1, wherein thesubject has undergone a kidney transplant within 1 month, 3 months, 1year, 2 years, 3 years or 5 years of performing step (a).
 13. The methodof claim 1, wherein step (b) is performed on at least 10, 20, 40, or 100genes.
 14. The method of claim 1, further comprising changing thetreatment regime of the subject responsive to the prognosing, detecting,diagnosing or monitoring step.
 15. The method of claim 14, wherein thesubject has received a drug before performing the methods, and thechanging the treatment regime comprises administering an additionaldrug, administering a higher dose of the same drug, administering alower dose of the same drug or stopping administering the same drug. 16.The method of claim 1, wherein the subject is prognosed or diagnosed tohave AR, have ADNR, have CAN, or have TX, and wherein the at least fivegenes are selected from the at least one of Tables 7, 8, 9, 10, and 11.17. The method of claim 1, wherein expression levels are determined atthe mRNA level or at the protein level.
 18. The method of claim 1,wherein step (c) is performed by a computer.
 19. An array, comprising asupport or supports bearing a plurality of nucleic acid probescomplementary to a plurality of mRNAs fewer than 5000 in number, whereinthe plurality of mRNAs includes mRNAs expressed by at least five genesselected from the at least one of Tables 7, 8, 9, 10 and
 11. 20. Amethod of expression analysis, comprising determining expression levelsof up to 5000 genes in a sample from a subject having a kidneytransplant, wherein the genes include at least five genes selected fromthe at least one of Tables 7, 8, 9, 10, and 11.